Journal articles on the topic 'Optimal cluster density'
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Wang, Zheng, Changxin Liu, and Kejing Mao. "Industry cluster: spatial density and optimal scale." Annals of Regional Science 49, no. 3 (June 18, 2011): 719–31. http://dx.doi.org/10.1007/s00168-011-0452-6.
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Zhao, Chao, Junchuang Yang, and Kexin Wen. "An Improved Clustering Algorithm Based on Density Peak and Nearest Neighbors." Mathematical Problems in Engineering 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/5499213.
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Aiming at the problems that the initial cluster centers are randomly selected and the number of clusters is manually determined in traditional clustering algorithm, which results in unstable clustering results, we propose an improved clustering algorithm based on density peak and nearest neighbors. Firstly, an improved density peak clustering method is proposed to optimize the cutoff distance and local density of data points. It avoids that random selection of initial cluster centers is easy to fall into the local optimal solution. Furthermore, a K-value selection method is presented to choose the optimal number of clusters, which is determined by the sum of the squared errors within the clusters. Finally, we employ the idea of the K-nearest neighbors to carry out the assignment for outliers. Experiments on the UCI real data sets indicate that our proposed algorithm can achieve better clustering results compared with several known algorithms.
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Wang, Zhenggang, Xuantong Li, Jin Jin, Zhong Liu, and Wei Liu. "Unsupervised Clustering of Neighborhood Associations and Image Segmentation Applications." Algorithms 13, no. 12 (November 25, 2020): 309. http://dx.doi.org/10.3390/a13120309.
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Irregular shape clustering is always a difficult problem in clustering analysis. In this paper, by analyzing the advantages and disadvantages of existing clustering analysis algorithms, a new neighborhood density correlation clustering (NDCC) algorithm for quickly discovering arbitrary shaped clusters. Because the density of the center region of any cluster sample dataset is greater than that of the edge region, the data points can be divided into core, edge, and noise data points, and then the density correlation of the core data points in their neighborhood can be used to form a cluster. Further more, by constructing an objective function and optimizing the parameters automatically, a locally optimal result that is close to the globally optimal solution can be obtained. This algorithm avoids the clustering errors caused by iso-density points between clusters. We compare this algorithm with other five clustering algorithms and verify it on two common remote sensing image datasets. The results show that it can cluster the same ground objects in remote sensing images into one class and distinguish different ground objects. NDCC has strong robustness to irregular scattering dataset and can solve the clustering problem of remote sensing image.
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Tsegaye, Seneshaw, Thomas M. Missimer, Jong-Yeop Kim, and Jason Hock. "A Clustered, Decentralized Approach to Urban Water Management." Water 12, no. 1 (January 9, 2020): 185. http://dx.doi.org/10.3390/w12010185.
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Current models in design of urban water management systems and their corresponding infrastructure using centralized designs have commonly failed from the perspective of cost effectiveness and inability to adapt to the future changes. These challenges are driving cities towards using decentralized systems. While there is great consensus on the benefits of decentralization; currently no methods exist which guide decision-makers to define the optimal boundaries of decentralized water systems. A new clustering methodology and tool to decentralize water supply systems (WSS) into small and adaptable units is presented. The tool includes two major components: (i) minimization of the distance from source to consumer by assigning demand to the closest water source, and (ii) maximization of the intra-cluster homogeneity by defining the cluster boundaries such that the variation in population density, land use, socio-economic level, and topography within the cluster is minimized. The methodology and tool were applied to Arua Town in Uganda. Four random cluster scenarios and a centralized system were created and compared with the optimal clustered WSS. It was observed that the operational cost of the four cluster scenarios is up to 13.9 % higher than the optimal, and the centralized system is 26.6% higher than the optimal clustered WSS, consequently verifying the efficacy of the proposed method to determine an optimal cluster boundary for WSS. In addition, optimal homogeneous clusters improve efficiency by encouraging reuse of wastewater and stormwater within a cluster and by minimizing leakage through reduced pressure variations.
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Ren, Min, Peiyu Liu, Zhihao Wang, and Jing Yi. "A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2647389.
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For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.
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Srinivasa Raju, K., and D. Nagesh Kumar. "Selection of global climate models for India using cluster analysis." Journal of Water and Climate Change 7, no. 4 (March 28, 2016): 764–74. http://dx.doi.org/10.2166/wcc.2016.112.
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Global climate models (GCMs) are gaining importance due to their capability to ascertain climate variables that will be useful to develop long, medium and short term water resources planning strategies. The applicability of K-Means cluster analysis is explored for grouping 36 GCMs from Coupled Model Intercomparison Project 5 for maximum temperature (MAXT), minimum temperature (MINT) and a combination of maximum and minimum temperature (COMBT) over India. Cluster validation methods, namely the Davies–Bouldin Index (DBI) and F-statistic, are used to obtain an optimal number of clusters of GCMs for India. The indicator chosen for evaluation of GCMs is the probability density function based skill score. It is noticed that the optimal number of clusters for MAXT, MINT and COMBT scenarios are 3, 2 and 2, respectively. Accordingly, suitable ensembles of GCMs are suggested for India for MAXT, MINT and COMBT individually. The suggested methodology can be extended to any number of GCMs and indicators, with minor modifications.
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Zang, Wenke, Liyan Ren, Wenqian Zhang, and Xiyu Liu. "Automatic Density Peaks Clustering Using DNA Genetic Algorithm Optimized Data Field and Gaussian Process." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (May 9, 2017): 1750023. http://dx.doi.org/10.1142/s0218001417500239.
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Clustering by fast search and finding of Density Peaks (called as DPC) introduced by Alex Rodríguez and Alessandro Laio attracted much attention in the field of pattern recognition and artificial intelligence. However, DPC still has a lot of defects that are not resolved. Firstly, the local density [Formula: see text] of point [Formula: see text] is affected by the cutoff distance [Formula: see text], which can influence the clustering result, especially for small real-world cases. Secondly, the number of clusters is still found intuitively by using the decision diagram to select the cluster centers. In order to overcome these defects, this paper proposes an automatic density peaks clustering approach using DNA genetic algorithm optimized data field and Gaussian process (referred to as ADPC-DNAGA). ADPC-DNAGA can extract the optimal value of threshold with the potential entropy of data field and automatically determine the cluster centers by Gaussian method. For any data set to be clustered, the threshold can be calculated from the data set objectively rather than the empirical estimation. The proposed clustering algorithm is benchmarked on publicly available synthetic and real-world datasets which are commonly used for testing the performance of clustering algorithms. The clustering results are compared not only with that of DPC but also with that of several well-known clustering algorithms such as Affinity Propagation, DBSCAN and Spectral Cluster. The experimental results demonstrate that our proposed clustering algorithm can find the optimal cutoff distance [Formula: see text], to automatically identify clusters, regardless of their shape and dimension of the embedded space, and can often outperform the comparisons.
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Zhou, Rong, Yong Zhang, Shengzhong Feng, and Nurbol Luktarhan. "A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets." Complexity 2018 (July 18, 2018): 1–8. http://dx.doi.org/10.1155/2018/2032461.
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Clustering aims to differentiate objects from different groups (clusters) by similarities or distances between pairs of objects. Numerous clustering algorithms have been proposed to investigate what factors constitute a cluster and how to efficiently find them. The clustering by fast search and find of density peak algorithm is proposed to intuitively determine cluster centers and assign points to corresponding partitions for complex datasets. This method incorporates simple structure due to the noniterative logic and less few parameters; however, the guidelines for parameter selection and center determination are not explicit. To tackle these problems, we propose an improved hierarchical clustering method HCDP aiming to represent the complex structure of the dataset. A k-nearest neighbor strategy is integrated to compute the local density of each point, avoiding to select the nonnecessary global parameter dc and enables cluster smoothing and condensing. In addition, a new clustering evaluation approach is also introduced to extract a “flat” and “optimal” partition solution from the structure by adaptively computing the clustering stability. The proposed approach is conducted on some applications with complex datasets, where the results demonstrate that the novel method outperforms its counterparts to a large extent.
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SHIEH, HORNG-LIN, and CHENG-CHIEN KUO. "A NOVEL VALIDITY INDEX FOR THE SUBTRACTIVE CLUSTERING ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 04 (June 2011): 547–63. http://dx.doi.org/10.1142/s0218001411008798.
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This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. The SC algorithm continually produces the cluster centers until the final potential compared with the original is less than a predefined threshold. The procedure is terminated when there are only a few data points around the most recent cluster. The choice of the threshold is an important factor affecting the clustering results: if it is too large, then too few data points will be accepted as cluster centers; if it is too small, then too many cluster centers will be generated. In this paper, a modified SC algorithm for data clustering based on a cluster validity index is proposed to obtain the optimal number of clusters. Six examples show that the proposed index achieves better performance results than other cluster validities do.
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Tang, Mingzhu, Caihua Meng, Huawei Wu, Hongqiu Zhu, Jiabiao Yi, Jun Tang, and Yifan Wang. "Fault Detection for Wind Turbine Blade Bolts Based on GSG Combined with CS-LightGBM." Sensors 22, no. 18 (September 7, 2022): 6763. http://dx.doi.org/10.3390/s22186763.
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Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model–Synthetic Minority Oversampling Technique–Gaussian Mixture Model (GSG) combined with Cost-Sensitive LightGBM (CS-LightGBM) was proposed. Since it is difficult to obtain the fault samples of blade bolts, the GSG oversampling method was constructed to increase the fault samples in the blade bolt dataset. The method obtains the optimal number of clusters through the BIC criterion, and uses the GMM based on the optimal number of clusters to optimally cluster the fault samples in the blade bolt dataset. According to the density distribution of fault samples in inter-clusters, we synthesized new fault samples using SMOTE in an intra-cluster. This retains the distribution characteristics of the original fault class samples. Then, we used the GMM with the same initial cluster center to cluster the fault class samples that were added to new samples, and removed the synthetic fault class samples that were not clustered into the corresponding clusters. Finally, the synthetic data training set was used to train the CS-LightGBM fault detection model. Additionally, the hyperparameters of CS-LightGBM were optimized by the Bayesian optimization algorithm to obtain the optimal CS-LightGBM fault detection model. The experimental results show that compared with six models including SMOTE-LightGBM, CS-LightGBM, K-means-SMOTE-LightGBM, etc., the proposed fault detection model is superior to the other comparison methods in the false alarm rate, missing alarm rate and F1-score index. The method can well realize the fault detection of large wind turbine blade bolts.
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Zhu-Juan Ma, Zhu-Juan Ma, Zi-Han Wang Zhu-Juan Ma, Xiang-Hua Chen Zi-Han Wang, and Feng Liu Xiang-Hua Chen. "DP-Kmeans and Beyond: Optimal Clustering with a new Clustering Validity Index." 電腦學刊 33, no. 5 (October 2022): 001–17. http://dx.doi.org/10.53106/199115992022103305001.
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<p>The K-means clustering algorithm is widely used in many areas for its high efficiency. However, the performance of the traditional K-means algorithm is very sensitive to the selection of initial clustering centers. Furthermore, except the convex distributed datasets, the traditional K-means algorithm still cannot optimally process many non-convex distributed datasets and datasets with outliers. To this end, this paper proposes the DP-Kmeans, an improved K-means algorithm based on the Density Parameter and center replacement, which can be more accurate than the traditional K-means by dropping the random selection of the initial clustering centers and continuous updating of the new centers. Due to the unsupervised learning feature, the number of clusters and the quality of data partitions generated by the clustering algorithm cannot be guaranteed. In order to evaluate the results of the DP-Kmeans algorithm, this paper proposes the SII, a new clustering validity index based on the Sum of the Inner-cluster compactness and the Inter-cluster separateness. Based on the DP-Kmeans algorithm and the SII index, a new method is proposed to determine the optimal clustering numbers for different datasets. Experimental results on ten datasets with different distributions demonstrate that the proposed clustering method is more effective the existing ones. </p> <p> </p>
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Liu, Dong, Chuan Wang, and Yun Jing. "Estimating the optimal number of communities by cluster analysis." International Journal of Modern Physics B 30, no. 08 (March 30, 2016): 1650037. http://dx.doi.org/10.1142/s0217979216500375.
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How to identify community structure in complex network is of theoretical significance, which relates to help to analyze the network topology and understand the network works. Determining the optimal number of communities is a nontrivial problem in detecting community structure. In this paper, we propose a novel method for detecting the optimal number of communities. Based on the local random walk (LRW) measurement, the distance index between each pair of nodes of a network is calculated firstly. Then the optimal number of communities can be found based on the idea that community centers are characterized by a higher density than their neighbors and by a relatively large distance from nodes with higher densities. The experimental results show that the method is effective and efficient in both artificial and real-world networks.
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Chen, Shou Gang, and Mei Yan Yu. "Theoretical Study on the Electronic Structures, Magnetic Properties and Corrosion-Resistant Ability of NinAl (n=1-8, 12) Clusters." Materials Science Forum 852 (April 2016): 55–64. http://dx.doi.org/10.4028/www.scientific.net/msf.852.55.
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The growth behavior, electronic structures and magnetic properties for NinAl (1-8,12) clusters were investigated detailedly using the selected density functional method (BPW91/LanL2DZ). The change of binding energies show that bimetallic clusters are more stable based on the computed bond energy of Al-Ni, which is bigger than that of Ni-Ni. The strong peaks of Ni5Al in run chart of binding energies, HOMO–LUMO gap, fragmentation energy, the second-order energy and the ionization potential indicate that the stability of bimetallic cluster is optimal when the doping of aluminum is 16.7 atomic percent. At the same time, the hardness analysis of bimetallic clusters shows that Ni5Al cluster has excellent corrosion resistance ability. In addition, the magnetic moment of NinAl (n=1-8,12) clusters decrease obviously comparing with pure nickel clusters because of the s-p-d hybridization between aluminum atom and nickel atom.
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Hasegawa, H., B. U. Ö. Sonnerup, B. Klecker, G. Paschmann, M. W. Dunlop, and H. Rème. "Optimal reconstruction of magnetopause structures from Cluster data." Annales Geophysicae 23, no. 3 (March 30, 2005): 973–82. http://dx.doi.org/10.5194/angeo-23-973-2005.
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Abstract. The Grad-Shafranov (GS) reconstruction technique, a single-spacecraft based data analysis method for recovering approximately two-dimensional (2-D) magnetohydrostatic plasma/field structures in space, is improved to become a multi-spacecraft technique that produces a single field map by ingesting data from all four Cluster spacecraft into the calculation. The plasma pressure, required for the technique, is measured in high time resolution by only two of the spacecraft, C1 and C3, but, with the help of spacecraft potential measurements available from all four spacecraft, the pressure can be estimated at the other spacecraft as well via a relationship, established from C1 and C3 data, between the pressure and the electron density deduced from the potentials. Consequently, four independent field maps, one for each spacecraft, can be reconstructed and then merged into a single map. The resulting map appears more accurate than the individual single-spacecraft based ones, in the sense that agreement between magnetic field variations predicted from the map to occur at each of the four spacecraft and those actually measured is significantly better. Such a composite map does not satisfy the GS equation any more, but is optimal under the constraints that the structures are 2-D and time-independent. Based on the reconstruction results, we show that, even on a scale of a few thousand km, the magnetopause surface is usually not planar, but has significant curvature, often with intriguing meso-scale structures embedded in the current layer, and that the thickness of both the current layer and the boundary layer attached to its earthward side can occasionally be larger than 3000km.
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Islam, Md Rashedul, Young-Hun Kim, Jae-Young Kim, and Jong-Myon Kim. "Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis." Applied Sciences 9, no. 11 (June 6, 2019): 2326. http://dx.doi.org/10.3390/app9112326.
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This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a new cluster evaluation method, i.e., multivariate probability density function’s cluster distribution factor (MPDFCDF). In this proposed model, a heterogeneous pool of features is constructed from the signal. A hybrid feature selection model is adopted for selecting optimal feature for learning the model with existing fault mode. The proposed online fault diagnosis system detects new fault modes from unknown signals using k-means clustering with the help of proposed MPDFCDF cluster evaluation method. The DSK is updated whenever new fault modes are detected and updated DSK is used to classify faults using the k-nearest neighbor (k-NN) classifier. The proposed model is evaluated using acoustic emission signals acquired from low-speed rolling element bearings with different fault modes and severities under different rotational speeds. Experimental results present that the MPDFCDF cluster evaluation method can detect the optimal number of fault clusters, and the proposed online diagnosis model can detect newly emerged faults and update the DSK effectively, which improves the diagnosis performance in terms of the average classification performance.
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Fernandez, L., M. M. Cueli, J. González-Nuevo, L. Bonavera, D. Crespo, J. M. Casas, and A. Lapi. "Galaxy cluster mass density profile derived using the submillimetre galaxies magnification bias." Astronomy & Astrophysics 658 (January 26, 2022): A19. http://dx.doi.org/10.1051/0004-6361/202141905.
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Context. The magnification bias is a gravitational lensing effect that produces an increase or decrease in the detection probability of background sources near the position of a lense. The special properties of the submillimetre galaxies (SMGs; steep source number counts, high redshift, and a very low cross-contamination with respect to the optical band) makes them the optimal background sample for magnification bias studies. Aims. We want to study the average mass density profile of tens to hundreds of clusters of galaxies acting as lenses that produce a magnification bias on the SMGs, and to estimate their associated masses and concentrations for different richness ranges. The cluster richness is defined as R = L200/L* with L200 as the total r-band luminosity within the radius r200. Methods. The background sample is composed of SMGs observed by Herschel with 1.2 < z < 4.0 (mean redshift at ∼2.3) while the foreground sample is made up of galaxy clusters extracted from the Sloan Digital Sky Survey III with photometric redshifts of 0.05 < z < 0.8 (mean redshift at ∼0.38). Measurements are obtained by stacking the SMG–cluster pairs to estimate the cross-correlation function using the Davis-Peebles estimator. This methodology allows us to derive the mass density profile for a wide range of angular scales, ∼2 − 250 arcsec or ∼10 − 1300 kpc for z = 0.38, with a high radial resolution, and in particular to study the inner part of the dark matter halo (< 100 kpc). In addition, we also divide the cluster sample into five bins of richness and we analyse the estimated cross-correlation data using different combinations of the most common theoretical mass density profiles. Results. It is impossible to fit the data with a single mass density profile at all scales: in the inner part there is a clear excess in the mass density profile with respect to the outer part that we interpret as the galactic halo of the big central galaxy. As for the outer part, the estimated average masses increase with richness from M200c = 5.8 × 1013 M⊙ to M200c = 51.5 × 1013 M⊙ (M200c = 7.1 × 1013 M⊙ for the total sample). With respect to the concentration parameter, its average also increases with richness from C = 0.74 to C = 1.74 (C = 1.72 for the total sample). In the small-scale regions, the obtained average masses fluctuate around M200c = 3 − 4 × 1013 M⊙ with average concentration values of around C ∼ 4. Conclusions. The total average masses are in perfect agreement with the mass–richness relationship estimated from the cluster catalogue. In the bins of lowest richness, the central galactic halo constitutes ∼40% of the total mass of the cluster and its relevance decreases for higher richness values. While the estimated average concentration values of the central galactic halos are in agreement with traditional mass–concentration relationships, we find low concentrations for the outer part. Moreover, the concentrations decrease for lower richness values, probably indicating that the group of galaxies cannot be considered to be relaxed systems. Finally, we notice a systematic lack of signal at the transition between the dominance of the cluster halo and the central galactic halo (∼100 kpc). This feature is also present in previous studies using different catalogues and/or methodologies, but is never discussed.
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Fatehi, Kavan, Mohsen Rezvani, Mansoor Fateh, and Mohammad-Reza Pajoohan. "Subspace Clustering for High-Dimensional Data Using Cluster Structure Similarity." International Journal of Intelligent Information Technologies 14, no. 3 (July 2018): 38–55. http://dx.doi.org/10.4018/ijiit.2018070103.
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This article describes how recently, because of the curse of dimensionality in high dimensional data, a significant amount of research has been conducted on subspace clustering aiming at discovering clusters embedded in any possible attributes combination. The main goal of subspace clustering algorithms is to find all clusters in all subspaces. Previous studies have mostly been generating redundant subspace clusters, leading to clustering accuracy loss and also increasing the running time of the algorithms. A bottom-up density-based approach is suggested in this article, in which the cluster structure serves as a similarity measure to generate the optimal subspaces which result in raising the accuracy of the subspace clustering. Based on this idea, the algorithm discovers similar subspaces by considering similarity in their cluster structure, then combines them and the data in the new subspaces would be clustered again. Finally, the algorithm determines all the subspaces and also finds all clusters within them. Experiments on various synthetic and real datasets show that the results of the proposed approach are significantly better in quality and runtime than the state-of-the-art on clustering high-dimensional data.
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Yang, Yingzhen, Xinqi Chu, Feng Liang, and Thomas Huang. "Pairwise Exemplar Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1204–11. http://dx.doi.org/10.1609/aaai.v26i1.8291.
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Exemplar-based clustering methods have been extensively shown to be effective in many clustering problems. They adaptively determine the number of clusters and hold the appealing advantage of not requiring the estimation of latent parameters, which is otherwise difficult in case of complicated parametric model and high dimensionality of the data. However, modeling arbitrary underlying distribution of the data is still difficult for existing exemplar-based clustering methods. We present Pairwise Exemplar Clustering (PEC) to alleviate this problem by modeling the underlying cluster distributions more accurately with non-parametric kernel density estimation. Interpreting the clusters as classes from a supervised learning perspective, we search for an optimal partition of the data that balances two quantities: 1 the misclassification rate of the data partition for separating the clusters; 2 the sum of within-cluster dissimilarities for controlling the cluster size. The broadly used kernel form of cut turns out to be a special case of our formulation. Moreover, we optimize the corresponding objective function by a new efficient algorithm for message computation in a pairwise MRF. Experimental results on synthetic and real data demonstrate the effectiveness of our method.
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Abdulrazzak, Hazem Noori, Goh Chin Hock, Nurul Asyikin Mohamed Radzi, Nadia M. L. Tan, and Chiew Foong Kwong. "Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network." Mathematics 10, no. 24 (December 12, 2022): 4720. http://dx.doi.org/10.3390/math10244720.
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Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively.
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Wei Liu, Wei Liu, Jiaxin Wang Wei Liu, Xiaopan Su Jiaxin Wang, and Yimin Mao Xiaopan Su. "MR-DBIFOA: a parallel Density-based Clustering Algorithm by Using Improve Fruit Fly Optimization." 電腦學刊 33, no. 1 (February 2022): 101–14. http://dx.doi.org/10.53106/199115992022023301010.
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<p>Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a division strategy (KDG) is proposed to divide the cell of grid adaptively. Secondly, an improve fruit fly optimization algorithm (IFOA) which use the step strategy based on knowledge learn (KLSS) and the clustering criterion function (CFF) is designed. In addition, based on IFOA algorithm, the optimal parameters of local clustering are dynamically selected, which can improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, the density-based clustering algorithm using IFOA (MR-QRMEC) are proposed to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce, a cluster merging algorithm (MR-QRMEC) is proposed to get the result of clustering algorithm more quickly, which improve the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA algorithm has better clustering results and performs better parallelization in big data.</p> <p> </p>
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Cojic, Milena M., Aleksandra Klisic, Radivoj Kocic, Andrej Veljkovic, and Gordana Kocic. "Data-Driven Cluster Analysis of Oxidative Stress Indexes in relation to Vitamin D Level, Age, and Metabolic Control in Patients with Type 2 Diabetes on Metformin Therapy." Oxidative Medicine and Cellular Longevity 2021 (June 21, 2021): 1–11. http://dx.doi.org/10.1155/2021/7942716.
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Recent advances in vitamin D research indicate that patients with type 2 diabetes mellitus (T2DM) are suffering from vitamin D deficiency and increased oxidative stress to a variable extent, which could produce different health impacts for each individual. The novel multivariate statistical method applied in the present study allows metabolic phenotyping of T2DM individuals based on vitamin D status, metabolic control, and oxidative stress status in order to identify effectively different subtypes in our type 2 DM study population. Data-driven statistical cluster analysis was performed with 95 patients with T2DM, treated with metformin. Clusters were based on 12 variables—age, disease duration, vitamin D level, insulin, fasting glycemia (FG), glycated hemoglobin (HbA1c), high-density and low-density lipoprotein, total cholesterol (TC), triglycerides (TG), body mass index (BMI), and triglycerides/glucose index (TYG). The analysis revealed four unique clusters which differed significantly in terms of vitamin D status, with a mean 25 (OH) D level in cluster 1 ( 57.84 ± 11.46 nmol/L) and cluster 4 ( 53.78 ± 22.36 nmol/L), falling within the insufficiency range. Cluster 2 had the highest mean level of 25 (OH) D ( 84.55 ± 22.66 nmol/L), indicative of vitamin D sufficiency. Cluster 3 had a mean vitamin D level below 50 nmol/L ( 49.27 ± 16.95 ), which is considered deficient. Patients in the vitamin D sufficient cluster had a significantly better glycemic and metabolic control as well as a lower level of lipid peroxidation compared to other clusters. The patients from the vitamin D sufficient cluster also had a significantly higher level of vitamin D/MPO, vitamin D/XO, vitamin D/MDA, vitamin D/CAT, and vitamin D/TRC than that in the vitamin deficient and insufficient clusters. The vitamin D deficient cluster included significantly younger patients and had a significantly lower level of AOPP/TRC and albumin/TRC than the vitamin D sufficient cluster. The evidence from our cluster analysis in the context of separated T2DM demonstrates beneficial effects of optimal vitamin D status on metabolic control and oxidative stress in T2DM patients. Older T2DM patients require higher vitamin D levels in order to achieve good metabolic control and favorable antioxidant protection. Since protein damage is more pronounced in these patients, adding water-soluble antioxidant in addition to higher doses of vitamin D should be considered.
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Guleria, Kalpna, and Anil Kumar Verma. "An energy efficient load balanced cluster-based routing using ant colony optimization for WSN." International Journal of Pervasive Computing and Communications 14, no. 3/4 (September 3, 2018): 233–46. http://dx.doi.org/10.1108/ijpcc-d-18-00013.
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Purpose Wireless sensor networks (WSNs) have emerged as one of the most promising technology in our day-to-day life. Limited network lifetime and higher energy consumption are two most critical issues in WSNs. The purpose of this paper is to propose an energy-efficient load balanced cluster-based routing protocol using ant colony optimization (LB-CR-ACO) which ultimately results in enhancement of the network lifetime of WSNs. Design/methodology/approach The proposed protocol performs optimal clustering based on cluster head selection weighing function which leads to novel cluster head selection. The cluster formation uses various parameters which are remaining energy of the nodes, received signal strength indicator (RSSI), node density and number of load-balanced node connections. Priority weights are also assigned among these metrics. The cluster head with the highest probability will be selected as an optimal cluster head for a particular round. LB-CR-ACO also performs a dynamic selection of optimal cluster head periodically which conserves energy, thereby using network resources in an efficient and balanced manner. ACO is used in steady state phase for multi-hop data transfer. Findings It has been observed through simulation that LB-CR-ACO protocol exhibits better performance for network lifetime in sparse, medium and dense WSN deployments than its peer protocols. Originality/value The proposed paper provides a unique energy-efficient LB-CR-ACO for WSNs. LB-CR-ACO performs novel cluster head selection using optimal clustering and multi-hop routing which utilizes ACO. The proposed work results in achieving higher network lifetime than its peer protocols.
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Lee, Jong-Yong, and Daesung Lee. "Improvement of cluster-based WSN protocol using fuzzy logic." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (September 1, 2020): 1540. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1540-1547.
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<span>A wireless sensor network is a collection of wireless nodes with sensor devices that can collect data from the real world. This is because sensor nodes usually use limited-powered batteries. Therefore, if the battery on the sensor node is exhausted, the node will no longer be available. If the battery on some nodes is discharged, the sensor network will not work properly. To maintain sensor network system, there are many wireless sensor network protocols to increase energy efficiency of nodes. One of the energy-efficient methods is cluster-based protocols. These protocols divide the sensor fields into clusters and send and receive data between nodes. Thus, depending on how the cluster is constructed, the network's lifetime may be reduced or increased. Cluster-based protocols cannot always be optimal cluster configurations. These problems have been improved using fuzzy logic. In general, fuzzy logic is used to elect cluster heads based on node residual energy, node concentration and node centrality. However, it is possible that nodes close to each other at a high density area are elected as cluster heads. In this paper, we propose a method to consider the number of adjacent cluster heads instead of Node Concentration to improve the problem.</span>
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Khan, Zahid, Anis Koubaa, Sangsha Fang, Mi Young Lee, and Khan Muhammad. "A Connectivity-Based Clustering Scheme for Intelligent Vehicles." Applied Sciences 11, no. 5 (March 9, 2021): 2413. http://dx.doi.org/10.3390/app11052413.
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The reliability, scalability, and stability of routing schemes are open challenges in highly evolving vehicular ad hoc networks (VANETs). Cluster-based routing is an efficient solution to cope with the dynamic and inconsistent structure of VANETs. In this paper, we propose a cluster-based routing scheme (hereinafter referred to as connectivity-based clustering), where link connectivity is used as a metric for cluster formation and cluster head (CH) selection. Link connectivity is a function of vehicle density and transmission range in the proposed connectivity-based clustering scheme. Moreover, we used a heuristic approach of spectral clustering for the optimal number of cluster formation. Lastly, an appropriate vehicle is selected as a CH based on the maximum Eigen-centrality score. The simulation results show that the suggested connectivity-based clustering scheme performs well in the optimal number of cluster selections, strongly connected (STC) route selection, and route request messages (RRMs) in the discovery of a particular path to the destination. Thus, we conclude that link connectivity and the heuristic approach of spectral clustering are valuable additions to existing routing schemes for high evolving networks.
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Najafi, Behzad, Paolo Bonomi, Andrea Casalegno, Fabio Rinaldi, and Andrea Baricci. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests." Energies 13, no. 14 (July 15, 2020): 3643. http://dx.doi.org/10.3390/en13143643.
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The present paper is focused on proposing and implementing a methodology for robust and rapid diagnosis of PEM fuel cells’ faults using Electrochemical Impedance Spectroscopy (EIS). Accordingly, EIS tests have been first conducted on four identical fresh PEM fuel cells along with an aged PEMFC at different current density levels and operating conditions. A label, which represents the presence of a type of fault (flooding or dehydration) or the regular operation, is then assigned to each test based on the expert knowledge employing the cell’s spectrum on the Nyquist plot. Since the time required to generate the spectrum should be minimized and considering the notable difference in the time needed for carrying out EIS tests at different frequency ranges, the frequencies have been categorized into four clusters (based on the corresponding order of magnitude: >1 kHz, >100 Hz, >10 Hz, >1 Hz). Next, for each frequency cluster and each specific current density, while utilizing a classification algorithm, a feature selection procedure is implemented in order to find the combination of EIS frequencies utilizing which results in the highest fault diagnosis accuracy and requires the lowest EIS testing time. For the case of fresh cells, employing the cluster of frequencies with f > 10 Hz, an accuracy of 98.5 % is obtained, whereas once the EIS tests from degraded cells are added to the dataset, the achieved accuracy is reduced to 89.2 % . It is also demonstrated that, while utilizing the selected pipelines, the required time for conducting the EIS test is less than one second, an advantage that facilitates real-time in-operando diagnosis of water management issues.
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Ahsan, Waleed, Muhammad Fahad Khan, Farhan Aadil, Muazzam Maqsood, Staish Ashraf, Yunyoung Nam, and Seungmin Rho. "Optimized Node Clustering in VANETs by Using Meta-Heuristic Algorithms." Electronics 9, no. 3 (February 27, 2020): 394. http://dx.doi.org/10.3390/electronics9030394.
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In a vehicular ad-hoc network (VANET), the vehicles are the nodes, and these nodes communicate with each other. On the road, vehicles are continuously in motion, and it causes a dynamic change in the network topology. It is more challenging when there is a higher node density. These conditions create many difficulties for network scalability and optimal route-finding in VANETs. Clustering protocols are being used frequently to solve such type of problems. In this paper, we proposed the grasshoppers’ optimization-based node clustering algorithm for VANETs (GOA) for optimal cluster head selection. The proposed algorithm reduced network overhead in unpredictable node density scenarios. To do so, different experiments were performed for comparative analysis of GOA with other state-of-the-art techniques like dragonfly algorithm, grey wolf optimizer (GWO), and ant colony optimization (ACO). Plentiful parameters, such as the number of clusters, network area, node density, and transmission range, were used in various experiments. The outcome of these results indicated that GOA outperformed existing methodologies. Lastly, the application of GOA in the flying ad-hoc network (FANET) domain was also proposed for next-generation networks.
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Bii, Nelson Kiprono, Christopher Ouma Onyango, and John Odhiambo. "Boundary Bias Correction Using Weighting Method in Presence of Nonresponse in Two-Stage Cluster Sampling." Journal of Probability and Statistics 2019 (June 2, 2019): 1–8. http://dx.doi.org/10.1155/2019/6812795.
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Kernel density estimators due to boundary effects are often not consistent when estimating a density near a finite endpoint of the support of the density to be estimated. To address this, researchers have proposed the application of an optimal bandwidth to balance the bias-variance trade-off in estimation of a finite population mean. This, however, does not eliminate the boundary bias. In this paper weighting method of compensating for nonresponse is proposed. Asymptotic properties of the proposed estimator of the population mean are derived. Under mild assumptions, the estimator is shown to be asymptotically consistent.
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Teich, Erin G., Greg van Anders, Daphne Klotsa, Julia Dshemuchadse, and Sharon C. Glotzer. "Clusters of polyhedra in spherical confinement." Proceedings of the National Academy of Sciences 113, no. 6 (January 25, 2016): E669—E678. http://dx.doi.org/10.1073/pnas.1524875113.
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Dense particle packing in a confining volume remains a rich, largely unexplored problem, despite applications in blood clotting, plasmonics, industrial packaging and transport, colloidal molecule design, and information storage. Here, we report densest found clusters of the Platonic solids in spherical confinement, for up to N=60 constituent polyhedral particles. We examine the interplay between anisotropic particle shape and isotropic 3D confinement. Densest clusters exhibit a wide variety of symmetry point groups and form in up to three layers at higher N. For many N values, icosahedra and dodecahedra form clusters that resemble sphere clusters. These common structures are layers of optimal spherical codes in most cases, a surprising fact given the significant faceting of the icosahedron and dodecahedron. We also investigate cluster density as a function of N for each particle shape. We find that, in contrast to what happens in bulk, polyhedra often pack less densely than spheres. We also find especially dense clusters at so-called magic numbers of constituent particles. Our results showcase the structural diversity and experimental utility of families of solutions to the packing in confinement problem.
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Chen, Nian, Kezhong Lu, and Hao Zhou. "A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy." Computational Intelligence and Neuroscience 2021 (June 22, 2021): 1–14. http://dx.doi.org/10.1155/2021/5592323.
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A band selection method based on two layers selection (TLS) strategy, which forms an optimal subset from all-bands set to reconstitute the original hyperspectral imagery (HSI) and aims to cost a fewer bands for better performances, is proposed in this paper. As its name implies, TLS picks out the bands with low correlation and a large amount of information into the target set to reach dimensionality reduction for HSI via two phases. Specifically, the fast density peaks clustering (FDPC) algorithm is used to select the most representative node in each cluster to build a candidate set at first. During the implementation, we normalize the local density and relative distance and utilize the dynamic cutoff distance to weaken the influence of density so that the selection is more likely to be carried out in scattered clusters than in high-density ones. After that, we conduct a further selection in the candidate set using mRMR strategy and comprehensive measurement of information (CMI), and the eventual winners will be selected into the target set. Compared with other six state-of-the-art unsupervised algorithms on three real-world HSI data sets, the results show that TLS can group the bands with lower correlation and richer information and has obvious advantages in indicators of overall accuracy (OA), average accuracy (AA), and Kappa coefficient.
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Wang, Xiao-Feng, and Yifan Xu. "Fast clustering using adaptive density peak detection." Statistical Methods in Medical Research 26, no. 6 (October 16, 2015): 2800–2811. http://dx.doi.org/10.1177/0962280215609948.
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Common limitations of clustering methods include the slow algorithm convergence, the instability of the pre-specification on a number of intrinsic parameters, and the lack of robustness to outliers. A recent clustering approach proposed a fast search algorithm of cluster centers based on their local densities. However, the selection of the key intrinsic parameters in the algorithm was not systematically investigated. It is relatively difficult to estimate the “optimal” parameters since the original definition of the local density in the algorithm is based on a truncated counting measure. In this paper, we propose a clustering procedure with adaptive density peak detection, where the local density is estimated through the nonparametric multivariate kernel estimation. The model parameter is then able to be calculated from the equations with statistical theoretical justification. We also develop an automatic cluster centroid selection method through maximizing an average silhouette index. The advantage and flexibility of the proposed method are demonstrated through simulation studies and the analysis of a few benchmark gene expression data sets. The method only needs to perform in one single step without any iteration and thus is fast and has a great potential to apply on big data analysis. A user-friendly R package ADPclust is developed for public use.
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Ng, Kelvin Sai-cheong, Man Hoi Lee, and Yongqiang Zong. "A Parameter for Quantifying the Macroscale Asymmetry of Tropical Cyclone Cloud Clusters." Journal of Atmospheric and Oceanic Technology 37, no. 9 (September 1, 2020): 1603–22. http://dx.doi.org/10.1175/jtech-d-19-0160.1.
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AbstractA parameter to quantify macroscale (i.e., systemwide) asymmetry of tropical cyclones (TC) in infrared satellite images, galaxy asymmetry (GASYM), which is adopted from astronomy, is described. In addition, an alternative approach to identify TC cloud clusters that is based on a density-based spatial clustering algorithm, cluster identification (CI), is presented in this study. Although a commonly used approach in TC study, the predefined radius of calculation (ROC), can be used to identify the TC region in the calculation of GASYM, this approach is not optimal because the size of the TC cloud cluster is often unknown in the calculation. The area specified by the ROC often includes pixels that do not belong to the TC cloud cluster and excludes pixels that belong to the TC cloud cluster. The CI approach addresses this issue by identifying TC cloud clusters of any size with any shape, because it depends solely on the threshold brightness temperature that corresponds to the upper bound of the brightness temperature of the specific cloud types. This study shows that the CI approach can be integrated into the GASYM calculation as an objective measure of TC symmetry. Although GASYM-CI and intensity are correlated, the relationship between GASYM-CI and intensity depends on the size of the TC cloud cluster. Comparison between GASYM and an existing objective method to quantify symmetry of TCs, the deviation angle variance technique, is also presented.
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Xia, Hong, Qingyi Dong, Hui Gao, Yanping Chen, and ZhongMin Wang. "Service Partition Method Based on Particle Swarm Fuzzy Clustering." Wireless Communications and Mobile Computing 2021 (December 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/7225552.
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It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.
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Weekley, R. Andrew, Robert K. Goodrich, and Larry B. Cornman. "An Algorithm for Classification and Outlier Detection of Time-Series Data." Journal of Atmospheric and Oceanic Technology 27, no. 1 (January 1, 2010): 94–107. http://dx.doi.org/10.1175/2009jtecha1299.1.
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Abstract An algorithm to perform outlier detection on time-series data is developed, the intelligent outlier detection algorithm (IODA). This algorithm treats a time series as an image and segments the image into clusters of interest, such as “nominal data” and “failure mode” clusters. The algorithm uses density clustering techniques to identify sequences of coincident clusters in both the time domain and delay space, where the delay-space representation of the time series consists of ordered pairs of consecutive data points taken from the time series. “Optimal” clusters that contain either mostly nominal or mostly failure-mode data are identified in both the time domain and delay space. A best cluster is selected in delay space and used to construct a “feature” in the time domain from a subset of the optimal time-domain clusters. Segments of the time series and each datum in the time series are classified using decision trees. Depending on the classification of the time series, a final quality score (or quality index) for each data point is calculated by combining a number of individual indicators. The performance of the algorithm is demonstrated via analyses of real and simulated time-series data.
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Sindi, Rooa, Cláudia Sá Dos Reis, Colleen Bennett, Gil Stevenson, and Zhonghua Sun. "Quantitative Measurements of Breast Density Using Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis." Journal of Clinical Medicine 8, no. 5 (May 24, 2019): 745. http://dx.doi.org/10.3390/jcm8050745.
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Breast density, a measure of dense fibroglandular tissue relative to non-dense fatty tissue, is confirmed as an independent risk factor of breast cancer. Although there has been an increasing interest in the quantitative assessment of breast density, no research has investigated the optimal technical approach of breast MRI in this aspect. Therefore, we performed a systematic review and meta-analysis to analyze the current studies on quantitative assessment of breast density using MRI and to determine the most appropriate technical/operational protocol. Databases (PubMed, EMBASE, ScienceDirect, and Web of Science) were searched systematically for eligible studies. Single arm meta-analysis was conducted to determine quantitative values of MRI in breast density assessments. Combined means with their 95% confidence interval (CI) were calculated using a fixed-effect model. In addition, subgroup meta-analyses were performed with stratification by breast density segmentation/measurement method. Furthermore, alternative groupings based on statistical similarities were identified via a cluster analysis employing study means and standard deviations in a Nearest Neighbor/Single Linkage. A total of 38 studies matched the inclusion criteria for this systematic review. Twenty-one of these studies were judged to be eligible for meta-analysis. The results indicated, generally, high levels of heterogeneity between study means within groups and high levels of heterogeneity between study variances within groups. The studies in two main clusters identified by the cluster analysis were also subjected to meta-analyses. The review confirmed high levels of heterogeneity within the breast density studies, considered to be due mainly to the applications of MR breast-imaging protocols and the use of breast density segmentation/measurement methods. Further research should be performed to determine the most appropriate protocol and method for quantifying breast density using MRI.
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Nguyen, Thuan Tan, Ban Van Doan, Chau Ngoc Truong, and Trinh Thi Thuy Tran. "Clustering and Query Optimization in Fuzzy Object-Oriented Database." International Journal of Natural Computing Research 8, no. 1 (January 2019): 1–17. http://dx.doi.org/10.4018/ijncr.2019010101.
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The purpose of the clustering method is to provide some meaningful partitioning of the data set. In general, finding separate clusters with similar members is essential. A problem in clustering is how to determine the number of optimal clusters that best fits the data set. Most clustering algorithms generate a partition based on input parameters (for example, cluster number, minimum density) which results in limiting the number of clusters. Therefore, the article proposes an improved EMC clustering algorithm that is more flexible in handling and manipulating those clusters, where input parameter values are assumed to be different clusters for different partitions of a data set. In addition, based on the above partitioning results, this article proposes a new approach to processing and optimizing fuzzy queries to improve efficiency in the manipulation and processing of specific data such as (less time consuming, less resource consuming)
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Yang, Jie, Yan Ma, Xiangfen Zhang, Shunbao Li, and Yuping Zhang. "An Initialization Method Based on Hybrid Distance for k-Means Algorithm." Neural Computation 29, no. 11 (November 2017): 3094–117. http://dx.doi.org/10.1162/neco_a_01014.
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The traditional [Formula: see text]-means algorithm has been widely used as a simple and efficient clustering method. However, the performance of this algorithm is highly dependent on the selection of initial cluster centers. Therefore, the method adopted for choosing initial cluster centers is extremely important. In this letter, we redefine the density of points according to the number of its neighbors, as well as the distance between points and their neighbors. In addition, we define a new distance measure that considers both Euclidean distance and density. Based on that, we propose an algorithm for selecting initial cluster centers that can dynamically adjust the weighting parameter. Furthermore, we propose a new internal clustering validation measure, the clustering validation index based on the neighbors (CVN), which can be exploited to select the optimal result among multiple clustering results. Experimental results show that the proposed algorithm outperforms existing initialization methods on real-world data sets and demonstrates the adaptability of the proposed algorithm to data sets with various characteristics.
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Ren, Biru, and Wei Liu. "Clustering Optimized Genetic Algorithm-Based 5G Communication base Station Site Selection." Highlights in Science, Engineering and Technology 24 (December 27, 2022): 1–6. http://dx.doi.org/10.54097/hset.v24i.3877.
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To minimize the negative impact of the base station construction, and at the same time make the base station cover as many users as possible, the Genetic Algorithm optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is proposed. Initially, the number of points is fixed and encoded into a gene sequence with 4 different features at each point. After that, the process of the DBSCAN is stated and then it constructs the cluster adaptation function. Then, with the combination of the cluster adaptation function and the coverage function, it can select the individual, cross the gene sequence, and change the slight value of the sequence. Ultimately, by iteration, it can output the optimal point location and the result shows that the optimal coverage is 95%.
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Luo, Naili, Wu Lin, Peizhi Huang, and Jianyong Chen. "An Evolutionary Algorithm with Clustering-Based Assisted Selection Strategy for Multimodal Multiobjective Optimization." Complexity 2021 (January 12, 2021): 1–13. http://dx.doi.org/10.1155/2021/4393818.
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In multimodal multiobjective optimization problems (MMOPs), multiple Pareto optimal sets, even some good local Pareto optimal sets, should be reserved, which can provide more choices for decision-makers. To solve MMOPs, this paper proposes an evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters by using a density-based clustering method, aiming to assist the addition operator to strengthen the population diversity. Then, a number of weight vectors are adopted to divide population into N subregions in objective space (N is population size). Moreover, in the deletion operator, the solutions in the most crowded subregion are first collected into previous clusters, and then the worst solution in the most crowded cluster is deleted until there are N solutions left. Our algorithm is compared with other multimodal multiobjective evolutionary algorithms on the well-known benchmark MMOPs. Numerical experiments report the effectiveness and advantages of our proposed algorithm.
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Zhang, Shichen, and Kai Li. "A Novel Density Peaks Clustering Algorithm with Isolation Kernel and K-Induction." Applied Sciences 13, no. 1 (December 27, 2022): 322. http://dx.doi.org/10.3390/app13010322.
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Density peaks clustering (DPC) algorithm can process data of any shape and is simple and intuitive. However, the distance between any two high-dimensional points tends to be consistent, which makes it difficult to distinguish the density peaks and easily produces “bad label” delivery. To surmount the above-mentioned defects, this paper put forward a novel density peaks clustering algorithm with isolation kernel and K-induction (IKDC). The IKDC uses an optimized isolation kernel instead of the traditional distance. The optimized isolation kernel solves the problem of converging the distance between the high-dimensional samples by increasing the similarity of two samples in a sparse domain and decreasing the similarity of two samples in a dense domain. In addition, the IKDC introduces three-way clustering, uses core domains to represent dense regions of clusters, and uses boundary domains to represent sparse regions of clusters, where points in the boundary domains may belong to one or more clusters. At the same time as determining the core domains, the improved KNN and average similarity are proposed to assign as many as possible to the core domains. The K-induction is proposed to assign the leftover points to the boundary domain of the optimal cluster. To confirm the practicability and validity of IKDC, we test on 10 synthetic and 8 real datasets. The comparison with other algorithms showed that the IKDC was superior to other algorithms in multiple clustering indicators.
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Rajeswari, A. R., K. Kulothungan, Sannasi Ganapathy, and Arputharaj Kannan. "Trusted energy aware cluster based routing using fuzzy logic for WSN in IoT." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 9197–211. http://dx.doi.org/10.3233/jifs-201633.
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WSN plays a major role in the design of IoT system. In today’s internet era IoT integrates the digital devices, sensing equipment and computing devices for data sensing, gathering and communicate the data to the Base station via the optimal path. WSN, owing to the characteristics such as energy constrained and untrustworthy environment makes them to face many challenges which may affect the performance and QoS of the network. Thus, in WSN based IoT both security and energy efficiency are considered as herculean design challenges and requires important concern for the enhancement of network life time. Hence, to address these problems in this paper a novel secure energy aware cluster based routing algorithm named Trusted Energy Efficient Fuzzy logic based clustering Algorithm (TEEFCA) has been proposed. This algorithm consists of two major objectives. Firstly, the trustworthy nodes are identified, which may act as candidate nodes for cluster based routing. Secondly, the fuzzy inference system is employed under the two circumstances namely selection of optimal Cluster Leader (CL) and cluster formation process by considering the following three parameters such as (i) node’s Residual Energy level (ii) Cluster Density (iii) Distance Node BS. From, the experiment outcomes implemented using MATLAB it have been proved that TEEFCA shows significant improvement in terms of power conservation, network stability and lifetime when compared to the existing cluster aware routing approaches.
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Naidu, D. J. Samatha, and S. Vijay Kumar. "IMPLEMENTATION OF DDOS ATTACKS USING K-MEANS CLUSTERING BASED ON SEMISUPERVISED ALGORITHMS." International Journal of Computer Science and Mobile Computing 11, no. 9 (September 30, 2022): 29–33. http://dx.doi.org/10.47760/ijcsmc.2022.v11i09.003.
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Distributed denial of service (DDoS) attack is an attempt to make an online service unavailable by overwhelming it with traffic from multiple sources. Presents a semi-supervised weighted k-means detection method. Hybrid feature selection algorithm to find the most effective feature sets and propose an improved density-based initial cluster centers selection algorithm to solve the problem of outliers and local optimal.
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Prabakar, D., and V. Saminadan. "Improving Spectral Efficiency of Small Cells with Multi-Variant Clustering and Interference Alignment." Journal of Computational and Theoretical Nanoscience 17, no. 5 (May 1, 2020): 2203–6. http://dx.doi.org/10.1166/jctn.2020.8871.
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In this article we introduce a multi-variant clustering with interference alignment (MVC-IA) for improving the spectral efficiency of small cells. Clustering process accounts the density and path loss factor of the communicating users to form efficient user group. The IA follows rank based interference identification and pre-coding for receiver power estimation. IA is restricted to the density of the users persuading clustering process to support a varying small cell density. This IA method addresses both inter and intra cluster cell interference by assigning optimal receiver for communicating in the allocated small cell channel. The performance of MVC-IA is verified for different transmit power and cell density through a comparative analysis.
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Wang, Limin, Wenjing Sun, Xuming Han, Zhiyuan Hao, Ruihong Zhou, Jinglin Yu, and Milan Parmar. "An Improved Integrated Clustering Learning Strategy Based on Three-Stage Affinity Propagation Algorithm with Density Peak Optimization Theory." Complexity 2021 (January 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6666619.
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To better reflect the precise clustering results of the data samples with different shapes and densities for affinity propagation clustering algorithm (AP), an improved integrated clustering learning strategy based on three-stage affinity propagation algorithm with density peak optimization theory (DPKT-AP) was proposed in this paper. DPKT-AP combined the ideology of integrated clustering with the AP algorithm, by introducing the density peak theory and k-means algorithm to carry on the three-stage clustering process. In the first stage, the clustering center point was selected by density peak clustering. Because the clustering center was surrounded by the nearest neighbor point with lower local density and had a relatively large distance from other points with higher density, it could help the k-means algorithm in the second stage avoiding the local optimal situation. In the second stage, the k-means algorithm was used to cluster the data samples to form several relatively small spherical subgroups, and each of subgroups had a local density maximum point, which is called the center point of the subgroup. In the third stage, DPKT-AP used the AP algorithm to merge and cluster the spherical subgroups. Experiments on UCI data sets and synthetic data sets showed that DPKT-AP improved the clustering performance and accuracy for the algorithm.
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Wang, Limin, Wenjing Sun, Xuming Han, Zhiyuan Hao, Ruihong Zhou, Jinglin Yu, and Milan Parmar. "An Improved Integrated Clustering Learning Strategy Based on Three-Stage Affinity Propagation Algorithm with Density Peak Optimization Theory." Complexity 2021 (January 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6666619.
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To better reflect the precise clustering results of the data samples with different shapes and densities for affinity propagation clustering algorithm (AP), an improved integrated clustering learning strategy based on three-stage affinity propagation algorithm with density peak optimization theory (DPKT-AP) was proposed in this paper. DPKT-AP combined the ideology of integrated clustering with the AP algorithm, by introducing the density peak theory and k-means algorithm to carry on the three-stage clustering process. In the first stage, the clustering center point was selected by density peak clustering. Because the clustering center was surrounded by the nearest neighbor point with lower local density and had a relatively large distance from other points with higher density, it could help the k-means algorithm in the second stage avoiding the local optimal situation. In the second stage, the k-means algorithm was used to cluster the data samples to form several relatively small spherical subgroups, and each of subgroups had a local density maximum point, which is called the center point of the subgroup. In the third stage, DPKT-AP used the AP algorithm to merge and cluster the spherical subgroups. Experiments on UCI data sets and synthetic data sets showed that DPKT-AP improved the clustering performance and accuracy for the algorithm.
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Szilagyi, Robert K., Rebecca Hanscam, Eric M. Shepard, and Shawn E. McGlynn. "Natural selection based on coordination chemistry: computational assessment of [4Fe–4S]-maquettes with non-coded amino acids." Interface Focus 9, no. 6 (October 18, 2019): 20190071. http://dx.doi.org/10.1098/rsfs.2019.0071.
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Cysteine is the only coded amino acid in biology that contains a thiol functional group. Deprotonated thiolate is essential for anchoring iron–sulfur ([Fe–S]) clusters, as prosthetic groups to the protein matrix. [Fe–S] metalloproteins and metalloenzymes are involved in biological electron transfer, radical chemistry, small molecule activation and signalling. These are key metabolic and regulatory processes that would likely have been present in the earliest organisms. In the context of emergence of life theories, the selection and evolution of the cysteine-specific R–CH 2 –SH side chain is a fascinating question to confront. We undertook a computational [4Fe–4S]-maquette modelling approach to evaluate how side chain length can influence [Fe–S] cluster binding and stability in short 7-mer and long 16-mer peptides, which contained either thioglycine, cysteine or homocysteine. Force field-based molecular dynamics simulations for [4Fe–4S] cluster nest formation were supplemented with density functional theory calculations of a ligand-exchange reaction between a preassembled cluster and the peptide. Secondary structure analysis revealed that peptides with cysteine are found with greater frequency nested to bind preformed [4Fe–4S] clusters. Additionally, the presence of the single methylene group in cysteine ligands mitigates the steric bulk, maintains the H-bonding and dipole network, and provides covalent Fe–S(thiolate) bonds that together create the optimal electronic and geometric structural conditions for [4Fe–4S] cluster binding compared to thioglycine or homocysteine ligands. Our theoretical work forms an experimentally testable hypothesis of the natural selection of cysteine through coordination chemistry.
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Du, Guoyu, Xuehua Li, Lanjie Zhang, Libo Liu, and Chaohua Zhao. "Novel Automated K-means++ Algorithm for Financial Data Sets." Mathematical Problems in Engineering 2021 (May 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/5521119.
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The K-means algorithm has been extensively investigated in the field of text clustering because of its linear time complexity and adaptation to sparse matrix data. However, it has two main problems, namely, the determination of the number of clusters and the location of the initial cluster centres. In this study, we propose an improved K-means++ algorithm based on the Davies-Bouldin index (DBI) and the largest sum of distance called the SDK-means++ algorithm. Firstly, we use the term frequency-inverse document frequency to represent the data set. Secondly, we measure the distance between objects by cosine similarity. Thirdly, the initial cluster centres are selected by comparing the distance to existing initial cluster centres and the maximum density. Fourthly, clustering results are obtained using the K-means++ method. Lastly, DBI is used to obtain optimal clustering results automatically. Experimental results on real bank transaction volume data sets show that the SDK-means++ algorithm is more effective and efficient than two other algorithms in organising large financial text data sets. The F-measure value of the proposed algorithm is 0.97. The running time of the SDK-means++ algorithm is reduced by 42.9% and 22.4% compared with that for K-means and K-means++ algorithms, respectively.
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Caruso, Giovanni, Giacomo Palai, Riccardo Gucci, and Simone Priori. "Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard." Applied Sciences 12, no. 3 (January 26, 2022): 1309. http://dx.doi.org/10.3390/app12031309.
Full textAbstract:
The aim of this study was to evaluate the potential use of remote and proximal sensing techniques to identify homogeneous zones in a high density irrigated olive (Olea europaea L.) orchard subjected to three irrigation regimes (full irrigation, deficit irrigation and rainfed conditions). An unmanned aerial vehicle equipped with a multispectral camera was used to measure the canopy NDVI and two different proximal soil sensors to map soil spatial variability at high resolution. We identified two clusters of trees showing differences in fruit yield (17.259 and 14.003 kg per tree in Cluster 1 and 2, respectively) and annual TCSA increment (0.26 and 0.24 dm2, respectively). The higher tree productivity measured in Cluster 1 also resulted in a higher water use efficiency for fruit (WUEf of 0.90 g dry weight L−1 H2O) and oil (WUEo of 0.32 g oil L−1 H2O) compared to Cluster 2 (0.67 and 0.27 for WUEf and WUEo, respectively). Remote and proximal sensing technologies allowed to determine that: (i) the effect of different irrigation regimes on tree performance and WUE depended on the location within the orchard; (ii) tree vigour played a major role in determining the final fruit yield under optimal soil water availability, whereas soil features prevailed under rainfed conditions.
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Cinaroglu, Songul, and Onur Baser. "Worldwide clustering of surgical indicators and predictors of risk of catastrophic expenditure for surgical care." Journal of Health Sciences 7, no. 3 (December 8, 2017): 188–95. http://dx.doi.org/10.17532/jhsci.2017.458.
Full textAbstract:
Introduction: Better access to surgical care is crucial to improve general health status of the population. Despite studies indicate cross country differences according to the general health indicators, there is a scarcity of knowledge about the differences between countries according to the surgical indicators. This study aims to classify countries according to the surgical care indicators and to identify predictors of risk of catastrophic health expenditure for surgical care.
Methods: Data came from WHO and WB statistics and totally 177 countries were selected for this study. Variable groups are determined as; total density of medical imaging technologies, workforce distribution in surgical care, number of surgical prodecures and risk of catastrophic expenditure for surgical care. K-means clustering algorithm was used to classify countries according to the surgical indicators. Optimal number of clusters determined by using within cluster sum of squares and scree plot. Silhouette index was used to examine clustering performance. Random Forest decision tree approach was used to determine predictors of the risk for catastrophic expenditure for surgical care.
Results: Study results shows that there are four country groups exists according to their surgical care indicators. High and low income countries are in different clusters. The third cluster which consists of low income countries has high Silhouette index value (0.75). Surgeon density and density of the medical imaging technologies are determinators of the risk for catastrophic expenditure for surgical care (AUC=0.82).
Conclusions: Study results pose that there is a need for more effective health plans to overcome the differences between countries in terms of surgical care indicators. Determining strategies about distribution of surgical workforce and medical imaging technologies considering accessibility and equality are recommendated for health policy makers.
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Tian, Ye, and Yasunari Yokota. "Estimating the Major Cluster by Mean-Shift with Updating Kernel." Mathematics 7, no. 9 (August 22, 2019): 771. http://dx.doi.org/10.3390/math7090771.
Full textAbstract:
The mean-shift method is a convenient mode-seeking method. Using a principle of the sample mean over an analysis window, or kernel, in a data space where samples are distributed with bias toward the densest direction of sample from the kernel center, the mean-shift method is an attempt to seek the densest point of samples, or the sample mode, iteratively. A smaller kernel leads to convergence to a local mode that appears because of statistical fluctuation. A larger kernel leads to estimation of a biased mode affected by other clusters, abnormal values, or outliers if they exist other than in the major cluster. Therefore, optimal selection of the kernel size, which is designated as the bandwidth in many reports of the literature, represents an important problem. As described herein, assuming that the major cluster follows a Gaussian probability density distribution, and, assuming that the outliers do not affect the sample mode of the major cluster, and, by adopting a Gaussian kernel, we propose a new mean-shift by which both the mean vector and covariance matrix of the major cluster are estimated in each iteration. Subsequently, the kernel size and shape are updated adaptively. Numerical experiments indicate that the mean vector, covariance matrix, and the number of samples of the major cluster can be estimated stably. Because the kernel shape can be adjusted not only to an isotropic shape but also to an anisotropic shape according to the sample distribution, the proposed method has higher estimation precision than the general mean-shift.
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Venhola, Aku, Reynier Peletier, Eija Laurikainen, Heikki Salo, Enrichetta Iodice, Steffen Mieske, Michael Hilker, et al. "The Fornax Deep Survey (FDS) with VST." Astronomy & Astrophysics 625 (May 2019): A143. http://dx.doi.org/10.1051/0004-6361/201935231.
Full textAbstract:
Context. Dwarf galaxies are the most common type of galaxies in galaxy clusters. Due to their low mass, they are more vulnerable to environmental effects than massive galaxies, and are thus optimal for studying the effects of the environment on galaxy evolution. By comparing the properties of dwarf galaxies with different masses, morphological types, and cluster-centric distances we can obtain information about the physical processes in clusters that play a role in the evolution of these objects and shape their properties. The Fornax Deep Survey Dwarf galaxy Catalog (FDSDC) includes 564 dwarf galaxies in the Fornax cluster and the in-falling Fornax A subgroup. This sample allows us to perform a robust statistical analysis of the structural and stellar population differences in the range of galactic environments within the Fornax cluster. Aims. By comparing our results with works concerning other clusters and the theoretical knowledge of the environmental processes taking place in galaxy clusters, we aim to understand the main mechanisms transforming galaxies in the Fornax cluster. Methods. We have exploited the FDSDC to study how the number density of galaxies, galaxy colors and structure change as a function of the cluster-centric distance, used as a proxy for the galactic environment and in-fall time. We also used deprojection methods to transform the observed shape and density distributions of the galaxies into the intrinsic physical values. These measurements are then compared with predictions of simple theoretical models of the effects of harassment and ram pressure stripping on galaxy structure. We used stellar population models to estimate the stellar masses, metallicities and ages of the dwarf galaxies. We compared the properties of the dwarf galaxies in Fornax with those in the other galaxy clusters with different masses. Results. We present the standard scaling relations for dwarf galaxies, which are the size-luminosity, Sérsic n-magnitude and color-magnitude relations. New in this paper is that we find a different behavior for the bright dwarfs (−18.5 mag < Mr′ < −16 mag) as compared to the fainter ones (Mr′ > −16 mag): While considering galaxies in the same magnitude-bins, we find that, while for fainter dwarfs the g′−r′ color is redder for lower surface brightness objects (as expected from fading stellar populations), for brighter dwarfs the color is redder for the higher surface brightness and higher Sérsic n objects. The trend of the bright dwarfs might be explained by those galaxies being affected by harassment and by slower quenching of star formation in their inner parts. As the fraction of early-type dwarfs with respect to late-types increases toward the central parts of the cluster, the color-surface brightness trends are also manifested in the cluster-centric trends, confirming that it is indeed the environment that changes the galaxies. We also estimate the strength of the ram-pressure stripping, tidal disruption, and harassment in the Fornax cluster, and find that our observations are consistent with the theoretically expected ranges of galaxy properties where each of those mechanisms dominate. We furthermore find that the luminosity function, color–magnitude relation, and axis-ratio distribution of the dwarfs in the center of the Fornax cluster are similar to those in the center of the Virgo cluster. This indicates that in spite of the fact that the Virgo is six times more massive, their central dwarf galaxy populations appear similar in the relations studied by us.