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1
Törn, A. A. "Clustering Methods in Global Optimization." IFAC Proceedings Volumes 19, no. 5 (May 1986): 247–52. http://dx.doi.org/10.1016/s1474-6670(17)59803-1.
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Rinnooy Kan, A. H. G., and G. T. Timmer. "Stochastic global optimization methods part I: Clustering methods." Mathematical Programming 39, no. 1 (September 1987): 27–56. http://dx.doi.org/10.1007/bf02592070.
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Bagattini, Francesco, Fabio Schoen, and Luca Tigli. "Clustering methods for large scale geometrical global optimization." Optimization Methods and Software 34, no. 5 (March 1, 2019): 1099–122. http://dx.doi.org/10.1080/10556788.2019.1582651.
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Schoen, Fabio, and Luca Tigli. "Efficient large scale global optimization through clustering-based population methods." Computers & Operations Research 127 (March 2021): 105165. http://dx.doi.org/10.1016/j.cor.2020.105165.
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Aldosari, Fahd, Laith Abualigah, and Khaled H. Almotairi. "A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications." Symmetry 14, no. 5 (May 17, 2022): 1021. http://dx.doi.org/10.3390/sym14051021.
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As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method’s findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance.
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Fong, Simon, Suash Deb, Xin-She Yang, and Yan Zhuang. "Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/564829.
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Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.
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Gerasina, O., V. Korniienko, O. Gusev, K. Sosnin, and S. Matsiuk. "Detecting fishing URLs using fuzzy clustering algorithms with global optimization." System technologies 2, no. 139 (March 30, 2022): 53–67. http://dx.doi.org/10.34185/1562-9945-2-139-2022-06.
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An algorithm for detecting phishing URLs (classifier) using fuzzy clustering is proposed, which includes choosing the type of intelligent classifier and justifying its parameters using global optimization methods. The following were studied as intellectual classifiers: subtractive clustering and fuzzy clustering of C-means. To find (adjust) the optimal (for a specific task) parameters of intelligent classifiers, the use of global optimization methods is justified, including genetic algorithm, direct random search, annealing simulation method, multicriteria optimization and threshold acceptance method. As a criterion of global optimization, a combined criterion was used, which includes the definition of the regularity criterion calculated on the test sample and the bias (minimum shift) criterion based on the analysis of solutions. By modeling in the Matlab environment with the help of standard and developed programs, the evaluated efficiency of using the proposed algorithm is evaluated on the example of experimental data – a set of 150 phishing and 150 secure URLs. The set of experimental data included information about the domain name registrar, the lifetime of the domain, the geolocation of the hosting server, the presence of a secure connection with a valid certificate. By simulation it is established that the fuzzy classifier with the subtractive clustering algorithm and using the Sugeno structure and 6 clusters meets the minimum of the combined criterion. All phishing URLs that were mistakenly classified as secure were found to have a secure con-nection with a valid certificate. Thus, further research should be aimed at exploring additional informative attributes (features) that could allow better separation of phishing and secure URLs.
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Duan, Yiqiang, Haoliang Yuan, Chun Sing Lai, and Loi Lei Lai. "Fusing Local and Global Information for One-Step Multi-View Subspace Clustering." Applied Sciences 12, no. 10 (May 18, 2022): 5094. http://dx.doi.org/10.3390/app12105094.
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Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.
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Wen, Guoqiu, Yonghua Zhu, Linjun Chen, Mengmeng Zhan, and Yangcai Xie. "Global and Local Structure Preservation for Nonlinear High-dimensional Spectral Clustering." Computer Journal 64, no. 7 (May 14, 2021): 993–1004. http://dx.doi.org/10.1093/comjnl/bxab020.
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Abstract Spectral clustering is widely applied in real applications, as it utilizes a graph matrix to consider the similarity relationship of subjects. The quality of graph structure is usually important to the robustness of the clustering task. However, existing spectral clustering methods consider either the local structure or the global structure, which can not provide comprehensive information for clustering tasks. Moreover, previous clustering methods only consider the simple similarity relationship, which may not output the optimal clustering performance. To solve these problems, we propose a novel clustering method considering both the local structure and the global structure for conducting nonlinear clustering. Specifically, our proposed method simultaneously considers (i) preserving the local structure and the global structure of subjects to provide comprehensive information for clustering tasks, (ii) exploring the nonlinear similarity relationship to capture the complex and inherent correlation of subjects and (iii) embedding dimensionality reduction techniques and a low-rank constraint in the framework of adaptive graph learning to reduce clustering biases. These constraints are considered in a unified optimization framework to result in one-step clustering. Experimental results on real data sets demonstrate that our method achieved competitive clustering performance in comparison with state-of-the-art clustering methods.
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Wang, Hong Chun, Feng Wen Wen, and Feng Song. "Clustering Algorithm Based on Improved Particle Swarm Optimization." Advanced Materials Research 765-767 (September 2013): 486–88. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.486.
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K-means algorithm has therefore become one of the methods widely used in cluster analysis. But the classification results of K-means algorithm depend on the initial cluster centers choice. We present a new neighborhood for PSO methods called the area of influence (AOI) and consider the combination of K-means has strong capacity of local searching and PSO has power global search ability. The improved PSO, i.e., improves the K-means local searching capacity, accelerates the convergence rate, and prevents the premature convergence effectively.
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Sharad, Er, Savita Shiwani, and Manish Suroliya. "Cluster head shuffling based global optimization using elephant herd optimization (EHO) approach." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 39. http://dx.doi.org/10.14419/ijet.v7i2.4.10039.
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Wireless Sensors are susceptible from frequent energy decay which leads to the reduction of lifetime of entire network scenario. Such energy loss occurring in the sensor nodes are addressed and worked out by number of researchers using number of methods including Low-energy adaptive clustering hierarchy (LEACH) and its number of variants. Despite of enormous variants of LEACH, there is still huge scope of research because of increasing use of sensor nodes in assorted scenarios. The development of energy aware wireless sensor networks is in research from a long time because of the increasing issues related to lesser lifetime of nodes in the wireless environment. The traditional lifetime of wireless nodes even in smart grids is 835 days while the other wireless nodes die in maximum 30 days. Many times, the battery time of wireless sensor nodes is very few days which is a costly affair. It is difficult and cost consuming to redeploy the wireless nodes to reform the network and cost of clustering. In this research work, a novel and performance aware approach Elephant Herd Optimization based Cluster Head Selection is developed and implemented so that the optimization level can be improved. The nature inspired soft computing approaches are prominently used for global optimization and reduction of error factors from existing results and that is the key focus in this research work.
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Zhu, Jian Min, Peng Du, and Ting Ting Fu. "Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods." Advanced Materials Research 317-319 (August 2011): 1529–36. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1529.
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The radial basis function (RBF) neural network is superior to other neural network on the aspects of approximation ability, classification ability, learning speed and global optimization etc., it has been widely applied as feedforward networks, its performance critically rely on the choice of RBF centers of network hidden layer node. K-means clustering, as a commonly method used on determining RBF center, has low neural network generalization ability, due to its clustering results are not sensitive to initial conditions and ignoring the influence of dependent variable. In view of this problem, fuzzy clustering and grey relational clustering methods are proposed to substitute K-means clustering, RBF center is determined by the results of fuzzy clustering or grey relational clustering, and some researches of RBF neural networks modeling accuracy are done. Practical modeling cases demonstrate that the modeling accuracy of fuzzy clustering RBF neural networks and grey relational clustering RBF neural networks are significantly better than K-means clustering RBF neural networks, applying of fuzzy clustering or grey relational clustering to determine the basis function center of RBF neural networks hidden layer node is feasible and effective.
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Jia, Yuheng, Hui Liu, Junhui Hou, and Qingfu Zhang. "Clustering Ensemble Meets Low-rank Tensor Approximation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7970–78. http://dx.doi.org/10.1609/aaai.v35i9.16972.
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This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one. The existing clustering ensemble methods generally construct a co-association matrix, which indicates the pairwise similarity between samples, as the weighted linear combination of the connective matrices from different base clusterings, and the resulting co-association matrix is then adopted as the input of an off-the-shelf clustering algorithm, e.g., spectral clustering. However, the co-association matrix may be dominated by poor base clusterings, resulting in inferior performance. In this paper, we propose a novel low-rank tensor approximation based method to solve the problem from a global perspective. Specifically, by inspecting whether two samples are clustered to an identical cluster under different base clusterings, we derive a coherent-link matrix, which contains limited but highly reliable relationships between samples. We then stack the coherent-link matrix and the co-association matrix to form a three-dimensional tensor, the low-rankness property of which is further explored to propagate the information of the coherent-link matrix to the co-association matrix, producing a refined co-association matrix. We formulate the proposed method as a convex constrained optimization problem and solve it efficiently. Experimental results over 7 benchmark data sets show that the proposed model achieves a breakthrough in clustering performance, compared with 12 state-of-the-art methods. To the best of our knowledge, this is the first work to explore the potential of low-rank tensor on clustering ensemble, which is fundamentally different from previous approaches. Last but not least, our method only contains one parameter, which can be easily tuned.
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Lin, Lijuan, Licheng Xing, Qingquan Jia, and Xuerui Zhang. "Research on the Partition Method Based on the Observation Points of Voltage Distortion in Harmonic Optimization." Journal of Physics: Conference Series 2253, no. 1 (April 1, 2022): 012005. http://dx.doi.org/10.1088/1742-6596/2253/1/012005.
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Abstract With the use of a large number of power electronic equipment, the harmonics in the power grid are becoming more and more serious. There are many methods for harmonic mitigation and optimization such as Global network Optimization. When there are many nodes, the global network optimization method is more complicated. Considering the problem of large numbers of harmonic injection nodes in the power grid, this paper proposes a method of clustering the voltage distortion nodes according to correlation, and using the characteristic nodes to describe the harmonic voltage distortion in the region to mitigate harmonic voltage. This method is based on the observation data of the voltage distortion of the nodes for dynamic selection and grouping. The results show that this method meets the needs of Global network Optimization. The data processing methods used in this paper such as feature extraction methods and time series interpolation optimization can effectively reduce the data dimension, and get better optimization effect.
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Yamanaka, Yoshikazu, and Katsutoshi Yoshida. "Simple gravitational particle swarm algorithm for multimodal optimization problems." PLOS ONE 16, no. 3 (March 18, 2021): e0248470. http://dx.doi.org/10.1371/journal.pone.0248470.
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In real world situations, decision makers prefer to have multiple optimal solutions before making a final decision. Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). Our GPSA is developed based on the concept of “particle clustering in the absence of clustering procedures”. Specifically, it simply replaces the global feedback term in classical particle swarm optimization (PSO) with an inverse-square gravitational force term between the particles. The gravitational force mutually attracts and repels the particles, enabling them to autonomously and dynamically generate sub-swarms in the absence of algorithmic clustering procedures. Most of the sub-swarms gather at the nearby global optima, but a small number of particles reach the distant optima. The niching behavior of our GPSA was tested first on simple MMO problems, and then on twenty MMO benchmark functions. The performance indices (peak ratio and success rate) of our GPSA were compared with those of existing niching PSOs (ring-topology PSO and fitness Euclidean-distance ratio PSO). The basic performance of our GPSA was comparable to that of the existing methods. Furthermore, an improved GPSA with a dynamic parameter delivered significantly superior results to the existing methods on at least 60% of the tested benchmark functions.
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Sabo, Kristian. "Center-based l1–clustering method." International Journal of Applied Mathematics and Computer Science 24, no. 1 (March 1, 2014): 151–63. http://dx.doi.org/10.2478/amcs-2014-0012.
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Abstract In this paper, we consider the l1-clustering problem for a finite data-point set which should be partitioned into k disjoint nonempty subsets. In that case, the objective function does not have to be either convex or differentiable, and generally it may have many local or global minima. Therefore, it becomes a complex global optimization problem. A method of searching for a locally optimal solution is proposed in the paper, the convergence of the corresponding iterative process is proved and the corresponding algorithm is given. The method is illustrated by and compared with some other clustering methods, especially with the l2-clustering method, which is also known in the literature as a smooth k-means method, on a few typical situations, such as the presence of outliers among the data and the clustering of incomplete data. Numerical experiments show in this case that the proposed l1-clustering algorithm is faster and gives significantly better results than the l2-clustering algorithm.
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Wang, Xun. "A Clustering Algorithm Based on Improved Particle Swarm Optimization." Applied Mechanics and Materials 635-637 (September 2014): 1467–70. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1467.
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K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.
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HU, TIANMING, JINZHI XIONG, and GENGZHONG ZHENG. "SIMILARITY-BASED COMBINATION OF MULTIPLE CLUSTERINGS." International Journal of Computational Intelligence and Applications 05, no. 03 (September 2005): 351–69. http://dx.doi.org/10.1142/s1469026805001660.
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Consensus clustering refers to combining multiple clusterings over a common set of objects into a single consolidated partition. After introducing the distribution-based view of partitions, we propose a series of entropy-based distance functions for comparing various partitions. Given a candidate partition set, consensus clustering is then formalized as an optimization problem of searching for a centroid partition with the smallest distance to that set. In addition to directly selecting the local centroid candidate, we also present two combining methods for the global centroid based on the new similarity determined by the whole candidate set. The centroid partition is likely to be top/middle-ranked in terms of closeness to the true partition. Finally we evaluate its effectiveness on both artificial and real datasets, with candidates from either the full space or the subspace.
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Zhu, Teng, Zhaozhong Gao, Tielan Huang, and Chen Shen. "Applying Particle Swarm Intelligence in PolSAR Image Clustering." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012064. http://dx.doi.org/10.1088/1742-6596/2025/1/012064.
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Abstract The clustering problem of polarimetric SAR image is an optimization problem with high dimension and large amount of data. Aiming at the problem that the classical unsupervised classification methods for High Resolution Polarimetric SAR images are difficult to find the global optimal solution. The Particle Swarm Optimization (PSO) algorithm was proposed in High Resolution PolSAR images clustering. For the first beginning, the scattering eigenvalues of PolSAR data were used for initial classification, and then followed by the computation of clustering center and initialization of PSO algorithm, finally the particle swarm are introduced in the iterative steps to reduce noise effect and improve the classification results. The performance of this novel method is demonstrated in experiments using L-Band PolSAR image of San Francisco Bay.
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Bhushan, S., and S. G. Antoshchuk. "A hybrid approach to energy efficient clustering for heterogeneous wireless sensor network." Технология и конструирование в электронной аппаратуре, no. 2 (2018): 15–20. http://dx.doi.org/10.15222/tkea2018.2.15.
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Meta-heuristic methods have shown good efficiency in solving optimization problems related to a wide range of practical applications in wireless sensor networks (WSN). Biogeography based optimization (BBO) is an evolutionary technique inspired by the migration of species between habitats which have been applied in solving global optimization problems. The article presents a hybrid approach for clustering wireless sensor networks that combines the meta-heuristic algorithm BBO, and K-environments. The simulation results show that the proposed approach (named KBBO) significantly improved the efficiency of such WSN parameters as stability time, lifetime, residual energy and throughput.
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Abualigah, Laith, Ali Diabat, and Raed Abu Zitar. "Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization." Mathematics 10, no. 23 (November 29, 2022): 4509. http://dx.doi.org/10.3390/math10234509.
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An efficient optimization method is needed to address complicated problems and find optimal solutions. The gazelle optimization algorithm (GOA) is a global stochastic optimizer that is straightforward to comprehend and has powerful search capabilities. Nevertheless, the GOA is unsuitable for addressing multimodal, hybrid functions, and data mining problems. Therefore, the current paper proposes the orthogonal learning (OL) method with Rosenbrock’s direct rotation strategy to improve the GOA and sustain the solution variety (IGOA). We performed comprehensive experiments based on various functions, including 23 classical and IEEE CEC2017 problems. Moreover, eight data clustering problems taken from the UCI repository were tested to verify the proposed method’s performance further. The IGOA was compared with several other proposed meta-heuristic algorithms. Moreover, the Wilcoxon signed-rank test further assessed the experimental results to conduct more systematic data analyses. The IGOA surpassed other comparative optimizers in terms of convergence speed and precision. The empirical results show that the proposed IGOA achieved better outcomes than the basic GOA and other state-of-the-art methods and performed better in terms of solution quality.
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Ying Yang, M., S. Feng, H. Ackermann, and B. Rosenhahn. "GLOBAL AND LOCAL SPARSE SUBSPACE OPTIMIZATION FOR MOTION SEGMENTATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 20, 2015): 475–82. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-475-2015.
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In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time.
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Yang, Jin Hui, and Xi Cao. "Clustering Algorithm Based on Improved Particle Swarm Algorithm." Advanced Materials Research 798-799 (September 2013): 689–92. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.689.
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K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.
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Abualigah, Laith Mohammad, Essam Said Hanandeh, Ahamad Tajudin Khader, Mohammed Abdallh Otair, and Shishir Kumar Shandilya. "An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 4 (May 7, 2020): 296–306. http://dx.doi.org/10.2174/1573405614666180903112541.
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Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.
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TAN, MENG P., JAMES R. BROACH, and CHRISTODOULOS A. FLOUDAS. "EVALUATION OF NORMALIZATION AND PRE-CLUSTERING ISSUES IN A NOVEL CLUSTERING APPROACH: GLOBAL OPTIMUM SEARCH WITH ENHANCED POSITIONING." Journal of Bioinformatics and Computational Biology 05, no. 04 (August 2007): 895–913. http://dx.doi.org/10.1142/s0219720007002941.
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We study the effects on clustering quality by different normalization and pre-clustering techniques for a novel mixed-integer nonlinear optimization-based clustering algorithm, the Global Optimum Search with Enhanced Positioning (EP_GOS_Clust). These are important issues to be addressed. DNA microarray experiments are informative tools to elucidate gene regulatory networks. But in order for gene expression levels to be comparable across microarrays, normalization procedures have to be properly undertaken. The aim of pre-clustering is to use an adequate amount of discriminatory characteristics to form rough information profiles, so that data with similar features can be pre-grouped together and outliers deemed insignificant to the clustering process can be removed. Using experimental DNA microarray data from the yeast Saccharomyces Cerevisiae, we study the merits of pre-clustering genes based on distance/correlation comparisons and symbolic representations such as {+, o, -}. As a performance metric, we look at the intra- and inter-cluster error sums, two generic but intuitive measures of clustering quality. We also use publicly available Gene Ontology resources to assess the clusters' level of biological coherence. Our analysis indicates a significant effect by normalization and pre-clustering methods on the clustering results. Hence, the outcome of this study has significance in fine-tuning the EP_GOS_Clust clustering approach.
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Gálvez, Jorge, Erik Cuevas, and Krishna Gopal Dhal. "A Competitive Memory Paradigm for Multimodal Optimization Driven by Clustering and Chaos." Mathematics 8, no. 6 (June 8, 2020): 934. http://dx.doi.org/10.3390/math8060934.
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Evolutionary Computation Methods (ECMs) are proposed as stochastic search methods to solve complex optimization problems where classical optimization methods are not suitable. Most of the proposed ECMs aim to find the global optimum for a given function. However, from a practical point of view, in engineering, finding the global optimum may not always be useful, since it may represent solutions that are not physically, mechanically or even structurally realizable. Commonly, the evolutionary operators of ECMs are not designed to efficiently register multiple optima by executing them a single run. Under such circumstances, there is a need to incorporate certain mechanisms to allow ECMs to maintain and register multiple optima at each generation executed in a single run. On the other hand, the concept of dominance found in animal behavior indicates the level of social interaction among two animals in terms of aggressiveness. Such aggressiveness keeps two or more individuals as distant as possible from one another, where the most dominant individual prevails as the other withdraws. In this paper, the concept of dominance is computationally abstracted in terms of a data structure called “competitive memory” to incorporate multimodal capabilities into the evolutionary operators of the recently proposed Cluster-Chaotic-Optimization (CCO). Under CCO, the competitive memory is implemented as a memory mechanism to efficiently register and maintain all possible optimal values within a single execution of the algorithm. The performance of the proposed method is numerically compared against several multimodal schemes over a set of benchmark functions. The experimental study suggests that the proposed approach outperforms its competitors in terms of robustness, quality, and precision.
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Kaur, Arvinder, and Yugal Kumar. "Analyzing Healthcare Data Using Water Wave Optimization-Based Clustering Technique." International Journal of Reliable and Quality E-Healthcare 10, no. 4 (October 2021): 38–57. http://dx.doi.org/10.4018/ijrqeh.2021100103.
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The medical informatics field gets wide attention among the research community while developing a disease diagnosis expert system for useful and accurate predictions. However, accuracy is one of the major medical informatics concerns, especially for disease diagnosis. Many researchers focused on the disease diagnosis system through computational intelligence methods. Hence, this paper describes a new diagnostic model for analyzing healthcare data. The proposed diagnostic model consists of preprocessing, diagnosis, and performance evaluation phases. This model implements the water wave optimization (WWO) algorithm to analyze the healthcare data. Before integrating the WWO algorithm in the proposed model, two modifications are inculcated in WWO to make it more robust and efficient. These modifications are described as global information component and mutation operator. Several performance indicators are applied to assess the diagnostic model. The proposed model achieves better results than existing models and algorithms.
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Li, Yaping. "Glowworm Swarm Optimization Algorithm- and K-Prototypes Algorithm-Based Metadata Tree Clustering." Mathematical Problems in Engineering 2021 (February 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/8690418.
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The main objective of this paper is to present a new clustering algorithm for metadata trees based on K-prototypes algorithm, GSO (glowworm swarm optimization) algorithm, and maximal frequent path (MFP). Metadata tree clustering includes computing the feature vector of the metadata tree and the feature vector clustering. Therefore, traditional data clustering methods are not suitable directly for metadata trees. As the main method to calculate eigenvectors, the MFP method also faces the difficulties of high computational complexity and loss of key information. Generally, the K-prototypes algorithm is suitable for clustering of mixed-attribute data such as feature vectors, but the K-prototypes algorithm is sensitive to the initial clustering center. Compared with other swarm intelligence algorithms, the GSO algorithm has more efficient global search advantages, which are suitable for solving multimodal problems and also useful to optimize the K-prototypes algorithm. To address the clustering of metadata tree structures in terms of clustering accuracy and high data dimension, this paper combines the GSO algorithm, K-prototypes algorithm, and MFP together to study and design a new metadata structure clustering method. Firstly, MFP is used to describe metadata tree features, and the key parameter of categorical data is introduced into the feature vector of MFP to improve the accuracy of the feature vector to describe the metadata tree; secondly, GSO is combined with K-prototypes to design GSOKP for clustering the feature vector that contains numeric data and categorical data so as to improve the clustering accuracy; finally, tests are conducted with a set of metadata trees. The experimental results show that the designed metadata tree clustering method GSOKP-FP has certain advantages in respect to clustering accuracy and time complexity.
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Martínez-Muñoz, David, Jose García, Jose V. Martí, and Víctor Yepes. "Hybrid Swarm Intelligence Optimization Methods for Low-Embodied Energy Steel-Concrete Composite Bridges." Mathematics 11, no. 1 (December 27, 2022): 140. http://dx.doi.org/10.3390/math11010140.
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Bridge optimization is a significant challenge, given the huge number of possible configurations of the problem. Embodied energy and cost were taken as objective functions for a box-girder steel–concrete optimization problem considering both as single-objective. Embodied energy was chosen as a sustainable criterion to compare the results with cost. The stochastic global search TAMO algorithm, the swarm intelligence cuckoo search (CS), and sine cosine algorithms (SCA) were used to achieve this goal. To allow the SCA and SC techniques to solve the discrete bridge optimization problem, the discretization technique applying the k-means clustering technique was used. As a result, SC was found to produce objective energy function values comparable to TAMO while reducing the computation time by 25.79%. In addition, the cost optimization and embodied energy analysis revealed that each euro saved using metaheuristic methodologies decreased the energy consumption for this optimization problem by 0.584 kW·h. Additionally, by including cells in the upper and lower parts of the webs, the behavior of the section was improved, as were the optimization outcomes for the two optimization objectives. This study concludes that double composite action design on supports makes the continuous longitudinal stiffeners in the bottom flange unnecessary.
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Wang, Lin, Xiyu Liu, Minghe Sun, Jianhua Qu, and Yanmeng Wei. "A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems." Mathematical Problems in Engineering 2018 (August 19, 2018): 1–14. http://dx.doi.org/10.1155/2018/8250480.
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A new method using collective responses of starling birds is developed to enhance the global search performance of standard particle swarm optimization (PSO). The method is named chaotic starling particle swarm optimization (CSPSO). In CSPSO, the inertia weight is adjusted using a nonlinear decreasing approach and the acceleration coefficients are adjusted using a chaotic logistic mapping strategy to avoid prematurity of the search process. A dynamic disturbance term (DDT) is used in velocity updating to enhance convergence of the algorithm. A local search method inspired by the behavior of starling birds utilizing the information of the nearest neighbors is used to determine a new collective position and a new collective velocity for selected particles. Two particle selection methods, Euclidean distance and fitness function, are adopted to ensure the overall convergence of the search process. Experimental results on benchmark function optimization and classic clustering problems verified the effectiveness of this proposed CSPSO algorithm.
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Zhang, Tao, Changfu Yang, and Xin Zhao. "Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning." Applied Sciences 9, no. 5 (February 28, 2019): 866. http://dx.doi.org/10.3390/app9050866.
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Today, more and more complex tasks are emerging. To finish these tasks within a reasonable time, using the complex embedded system which has multiple processing units is necessary. Hardware/software partitioning is one of the key technologies in designing complex embedded systems, it is usually taken as an optimization problem and be solved with different optimization methods. Among the optimization methods, swarm intelligent (SI) algorithms are easily applied and have the advantages of strong robustness and excellent global search ability. Due to the high complexity of hardware/software partitioning problems, the SI algorithms are ideal methods to solve the problems. In this paper, a new SI algorithm, called brainstorm optimization (BSO), is applied to hardware/software partitioning. In order to improve the performance of the BSO, we analyzed its optimization process when solving the hardware/software partitioning problem and found the disadvantages in terms of the clustering method and the updating strategy. Then we proposed the improved brainstorm optimization (IBSO) which ameliorated the original clustering method by setting the cluster points and improved the updating strategy by decreasing the number of updated individuals in each iteration. Based on the simulation methods which are usually used to evaluate the performance of the hardware/software partitioning algorithms, we generated eight benchmarks which represent tasks with different scales to test the performance of IBSO, BSO, four original heuristic algorithms and two improved BSO. Simulation results show that the IBSO algorithm can achieve the solutions with the highest quality within the shortest running time among these algorithms.
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Yang, Kai, Kaiping Yu, and Hui Wang. "A hybrid method of multi-objective particle swarm optimization and k-means clustering and its application to modal parameter estimation in the time–frequency domain." Journal of Vibration and Control 26, no. 9-10 (November 25, 2019): 769–78. http://dx.doi.org/10.1177/1077546319889787.
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Modal parameters provide an insight into the dynamical properties of structures. In the time–frequency domain–based methods, time–frequency ridges contain crucial information on the characteristics of multicomponent signals, and manually extracting time–frequency ridges is a huge burden, especially when long-time time-varying modal parameters are focused on. In this study, time–frequency ridge extraction is converted into a multi-objective optimization problem, and a new hybrid method of multi-objective particle swarm optimization and k-means clustering is proposed to solve such a multi-objective optimization problem. In the hybrid method, the particle swarm is partitioned into sub-swarms by k-means clustering, and the sub-swarms are used to search new solutions for updating a finite-sized external archive, which is used as the exclusive centroids of the k-means clustering. Simultaneously, the finite-sized external archive serves as global best positions of sub-swarms. Both simulated and experimental cases are applied to validate the hybrid method. With the aid of the hybrid method, the influence of varying temperatures on modal parameters of a column beam is experimentally analyzed in detail.
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Alotaibi, Youseef. "A New Meta-Heuristics Data Clustering Algorithm Based on Tabu Search and Adaptive Search Memory." Symmetry 14, no. 3 (March 20, 2022): 623. http://dx.doi.org/10.3390/sym14030623.
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Clustering is a popular data analysis and data mining problem. Symmetry can be considered as a pre-attentive feature, which can improve shapes and objects, as well as reconstruction and recognition. The symmetry-based clustering methods search for clusters that are symmetric with respect to their centers. Furthermore, the K-means (K-M) algorithm can be considered as one of the most common clustering methods. It can be operated more quickly in most conditions, as it is easily implemented. However, it is sensitively initialized and it can be easily trapped in local targets. The Tabu Search (TS) algorithm is a stochastic global optimization technique, while Adaptive Search Memory (ASM) is an important component of TS. ASM is a combination of different memory structures that save statistics about search space and gives TS needed heuristic data to explore search space economically. Thus, a new meta-heuristics algorithm called (MHTSASM) is proposed in this paper for data clustering, which is based on TS and K-M. It uses TS to make economic exploration for data with the help of ASM. It starts with a random initial solution. It obtains neighbors of the current solution called trial solutions and updates memory elements for each iteration. The intensification and diversification strategies are used to enhance the search process. The proposed MHTSASM algorithm performance is compared with multiple clustering techniques based on both optimization and meta-heuristics. The experimental results indicate the superiority of the MHTSASM algorithm compared with other multiple clustering algorithms.
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Pramana, Setia, and Imam Habib Pamungkas. "Improvement Method of Fuzzy Geographically Weighted Clustering using Gravitational Search Algorithm." Jurnal Ilmu Komputer dan Informasi 11, no. 1 (February 28, 2018): 10. http://dx.doi.org/10.21609/jiki.v11i1.580.
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Geo-demographic analysis (GDA) is a useful method to analyze information based on location, utilizing several spatial analysis explicitly. One of the most efficient and commonly used method is Fuzzy Geographically Weighted Clustering (FGWC). However, it has a limitation in obtaining local optimal solution in the centroid initialization. A novel approach integrating Gravitational Search Algorithm (GSA) with FGWC is proposed to obtain global optimal solution leading to better cluster quality. Several cluster validity indexes are used to compare the proposed methods with the FGWC using other optimization approaches. The study shows that the hybrid method FGWC-GSA provides better cluster quality. Furthermore, the method has been implemented in R package spatialClust.
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Karima, Kies, and Benamrane Nacera. "A Dynamic Particle Swarm Optimisation and Fuzzy Clustering Means Algorithm for Segmentation of Multimodal Brain Magnetic Resonance Image Data." International Arab Journal of Information Technology 17, no. 6 (November 1, 2020): 976–83. http://dx.doi.org/10.34028/iajit/17/6/16.
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Fuzzy Clustering Means (FCM) algorithm is a widely used clustering method in image segmentation, but it often falls into local minimum and is quite sensitive to initial values which are random in most cases. In this work, we consider the extension to FCM to multimodal data improved by a Dynamic Particle Swarm Optimization (DPSO) algorithm which by construction incorporates local and global optimization capabilities. Image segmentation of three-variate MRI brain data is achieved using FCM-3 and DPSOFCM-3 where the three modalities T1-weighted, T2-weighted and Proton Density (PD), are treated at once (the suffix -3 is added to distinguish our three-variate method from mono-variate methods usually using T1-weighted modality). FCM-3 and DPSOFCM-3 were evaluated on several Magnetic Resonance (MR) brain images corrupted by different levels of noise and intensity non-uniformity. By means of various performance criteria, our results show that the proposed method substantially improves segmentation results. For noisiest and most no-uniform images, the performance improved as much as 9% with respect to other methods
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Miao, Kehua, Jialin Wang, Yunlong Gao, Chao Cao, Youwei Xie, and Peng Gao. "Robust Fuzzy Clustering Algorithm Based on Adaptive Neighbors." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012046. http://dx.doi.org/10.1088/1742-6596/2025/1/012046.
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Abstract In pattern recognition, fuzzy logic is widely used in unsupervised classification or clustering methods. Fuzzy c-means (FCM) clustering algorithm is a typical dynamic clustering algorithm of the fuzzy c-means algorithm based on the error square sum criterion. It introduces fuzzy membership and optimizes the objective function to obtain each sample point for all classes. The membership degree of the center is used to automatically classify the data sample. However, the FCM algorithm is susceptible to the noise points and outliers, and the unbalanced data structure reduces the generalization performance of the algorithm. In this paper, we propose a fuzzy c-means clustering algorithm with adaptive neighbors weight learning. Through adaptive neighborhood robust weight learning, an adaptive weight vector with robustness and sparsity is obtained. During the optimization, we only activate the k samples with the shortest distance to the cluster center and eliminate extreme noise samples to improve the global robustness and sparsity of the algorithm. Finally, empirical analysis verifies the superiority and effectiveness of the proposed clustering method.
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Zhao, Jie, Honghai Guan, Changpeng Lu, and Yushu Zheng. "Evaluation of Teachers’ Educational Technology Ability Based on Fuzzy Clustering Generalized Regression Neural Network." Computational Intelligence and Neuroscience 2021 (September 13, 2021): 1–10. http://dx.doi.org/10.1155/2021/1867723.
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The improvement of teachers’ educational technology ability is one of the main methods to improve the management efficiency of colleges and universities in China, and the scientific evaluation of teachers’ ability is of great significance. In view of this, this study proposes an evaluation model of teachers’ educational technology ability based on the fuzzy clustering generalized regression neural network. Firstly, the comprehensive evaluation structure system of teachers’ educational technology ability is constructed, and then the prediction method of teachers’ ability based on fuzzy clustering algorithm is analysed. On this basis, the optimization prediction method of fuzzy clustering generalized regression neural network is proposed. Finally, the application effect of fuzzy clustering generalized regression neural network in the evaluation of teachers’ educational technology ability is analysed. The results show that the evaluation system of teachers’ educational technology ability proposed in this study is scientific and reasonable; fuzzy clustering generalized regression neural network model can better accurately predict the ability of teachers’ educational technology and can quickly realize global optimization. According to the fitness analysis results of the fuzzy clustering generalized regression neural network model, the model converges after the 20th iteration and the fitness value remains about 1.45. Therefore, the fuzzy clustering generalized regression neural network has stronger adaptability and has been optimized to a certain extent. The average evaluation accuracy of fuzzy clustering generalized regression neural network model is 98.44%, and the evaluation results of the model are better than other algorithms. It is hoped that this study can provide some reference value for the evaluation of teachers’ educational technology ability in colleges and universities in China.
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Kilinc, Muslum, and Juan M. Caicedo. "Finding Plausible Optimal Solutions in Engineering Problems Using an Adaptive Genetic Algorithm." Advances in Civil Engineering 2019 (February 27, 2019): 1–9. http://dx.doi.org/10.1155/2019/7475156.
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In engineering, optimization applications are commonly used to solve various problems. As widely known, solution of an engineering problem does not have a unique result; moreover, the solution of a unique problem may totally differ from one engineer to another. On the other hand, one of the most commonly used engineering optimization methods is genetic algorithm that leads us to only one global optimum. As to mention, engineering problems can conclude in different results from the point of different engineers’ views. In this study, a modified genetic algorithm named multi-solution genetic algorithm (MsGA) based on clustering and section approaches is presented to identify alternative solutions for an engineering problem. MsGA can identify local optima points along with global optimum and can find numerous solution alternatives. The reliability of MsGA was tested by using a Gaussian and trigonometric function. After testing, MsGA was applied to a truss optimization problem as an example of an engineering optimization problem. The result obtained shows that MsGA is successful at finding multiple plausible solutions to an engineering optima problem.
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Sharma, Divya, and Shikha Lohchab. "Search based Software Modularization Using Evolution Algorithm." NeuroQuantology 20, no. 5 (May 18, 2022): 822–31. http://dx.doi.org/10.14704/nq.2022.20.5.nq22240.
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To comprehend a Software system, Software modularization strategies are used. The goal of modularization is to break down a software system into meaningful and intelligible sub-systems from its source-code (modules). Because software classification modularization is an NP-hard task, evolutionary methods produce better modularization quality rather than avaricious algorithms. All available transformative techniques for software modularization only take into account structural aspects reliant on programming language syntax. Because most computer languages lack a mechanism for extracting structural characteristics, they cannot be modularized. A novel heuristic is proposed in this work. with several objectives that accomplishes both in order to lead optimization algorithms towards a proper decomposition of software systems automatically, structural (e.g.; calling dependence and in-heritance dependency) and non- structural (e.g.; semantics in code comments and identifier names) aspects are used. It is analyzed using 3 optimization plans, viz; global-based-search, combining global and local search, and Estimation of Distribution (EoD) to upgrade it. According to outcomes on Mozilla Firefox, suggested optimization algorithm based on EoD and the newly developed MOF function exceed those so it use structural-based objective functions in finding more understandable modules, as well as guiding the optimization procedure. In the lack of a unique concept, structure, the original design can be identified by using the source code of the disturbed software. Effective software maintenance depends on the concept of software system. One of most powerful techniques in software clustering is the ability to divide enormous Software systems into workable subsystems with modules of identical characteristics, thereby reducing the complexity of the system. A metaheuristic optimization imperialist competitive system has emerged algorithm, genetic algorithm, and their combination is examined for software clustering in this paper. When it comes to value, of clustering, the number of epochs required for convergence, and the standard unconventionality found at the end of repeated application of these algorithms, it appears that recursive application is the most effective for achieving the best performance.
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Xu, Mengying, and Jie Zhou. "A Biologically Inspired Algorithm for Low Energy Clustering Problem in Body Area Network." Journal of Sensors 2021 (April 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/5525602.
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The growing application of body area networks (BANs) in different fields makes the low energy clustering a paramount issue. A clustering optimization algorithm in BANs is a fundamental scheme to guarantee that the essential collected data can be forwarded in a reliable path and improve the lifetime of BANs. Low energy clustering is a technique, which provides a method that shows how to reduce network communication costs in BANs. A careful low energy clustering scheme is one of the most critical means in the research of BANs, which has attracted considerable attention, comprising monitoring capability constraints. However, the classical clustering method leads to high cost when constraints such as large overall energy consumption are undertaken. Hence, a binary immune hybrid artificial bee colony algorithm (BIHABCA), a randomized swarm intelligent scheme applied in BANs, motivated by immune theory and hybrid scheme is introduced. Furthermore, we designed the formulation that considers both distances between two nodes and the length of bits. Finally, we have compared the energy cost optimized by BIHABCA with a shuffled frog leaping algorithm, ant colony optimization, and simulated annealing in the simulation with different quantity of nodes in terms of energy cost. Results show that the energy cost of the network optimized by the proposed BIHABCA method decreased compared to those by the other three methods which mean that the proposed BIHABCA finds the global optima and reduces the energy cost of transmitting and receiving data in BANs.
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Tanigawa, Yuya, Narayanan Krishnan, Eitaro Oomine, Atushi Yona, Hiroshi Takahashi, and Tomonobu Senjyu. "Clustering Method for Load Demand to Shorten the Time of Annual Simulation." Energies 16, no. 5 (February 27, 2023): 2264. http://dx.doi.org/10.3390/en16052264.
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UC (unit commitment) for grid operation has been attracting increasing attention due to the growing interest in global warming. Compared to other methods, MILP, which is one of the calculation methods for UC, has the disadvantage of a long calculation time, although it is more accurate in considering constraints and in finding solutions. However, RLCs (representative load curves) require a more accurate clustering method to select representative dates because the calculation results vary greatly depending on the clustering method. DBSCAN, one of the clustering methods, has the feature that the clustering accuracy varies depending on two parameters. Therefore, this paper proposes two algorithms to automatically determine the two parameters of DBSCAN to perform RLCs using DBSCAN. In addition, since DBSCAN has the feature of being able to represent different data as two-dimensional elements, a survey of the data to be used as clustering was conducted. As a result, the proposed algorithms enabled a more accurate clustering than the conventional method. It was also proved that clustering including temperature and load demand as clustering classification factors enables clustering with higher accuracy. The simulation with shorter time was also possible for the system including storage batteries as a demand response.
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Pizzagalli, Diego Ulisse, Santiago Fernandez Gonzalez, and Rolf Krause. "A trainable clustering algorithm based on shortest paths from density peaks." Science Advances 5, no. 10 (October 2019): eaax3770. http://dx.doi.org/10.1126/sciadv.aax3770.
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Clustering is a technique to analyze empirical data, with a major application for biomedical research. Essentially, clustering finds groups of related points in a dataset. However, results depend on both metrics for point-to-point similarity and rules for point-to-group association. Non-appropriate metrics and rules can lead to artifacts, especially in case of multiple groups with heterogeneous structure. In this work, we propose a clustering algorithm that evaluates the properties of paths between points (rather than point-to-point similarity) and solves a global optimization problem, finding solutions not obtainable by methods relying on local choices. Moreover, our algorithm is trainable. Hence, it can be adapted and adopted for specific datasets and applications by providing examples of valid and invalid paths to train a path classifier. We demonstrate its applicability to identify heterogeneous groups in challenging synthetic datasets, segment highly nonconvex immune cells in confocal microscopy images, and classify arrhythmic heartbeats in electrocardiographic signals.
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Bao, Zhanbiao. "Secure Clustering Strategy Based on Improved Particle Swarm Optimization Algorithm in Internet of Things." Computational Intelligence and Neuroscience 2022 (July 16, 2022): 1–9. http://dx.doi.org/10.1155/2022/7380849.
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This paper proposes a secure clustering strategy based on improved particle swarm optimization (PSO) in the environment of the Internet of Things (IoT). First, in the process of cluster head election, by considering the residual energy and load balance of nodes, a new fitness function is established to evaluate and select better candidate cluster head nodes. Second, the optimized adaptive learning factor is used to adjust the location update speed of candidate cluster head nodes, expand the local search, and accelerate the convergence speed of global search. Finally, in the stage of forwarding node election and data transmission, in order to reduce the energy consumption of forwarding nodes, each cluster head node elects a forwarding node among the ordinary nodes in its cluster, so that the elected forwarding nodes have the optimal energy and location relationship. Experiments show that the proposed method effectively prolongs the network lifetime compared with the comparison methods. The average node degree of the proposed method is less than 2.5.
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Zhang, Yaqiong, Jiyan Lin, and Hui Zhang. "A Hierarchical Teaching Mode of College Computer Basic Application Course Based on K-means and Improved PSO Algorithm." International Journal of Emerging Technologies in Learning (iJET) 11, no. 10 (October 27, 2016): 53. http://dx.doi.org/10.3991/ijet.v11i10.5909.
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As different students have different basics in learning College Computer Basic Application Course, so uniform teaching methods and curriculum cannot satisfy the needs of all of the students. To address this problem, an algorithm of student clustering which can achieve hierarchical teaching is designed in this paper. After analyzing the disadvantages of slow convergence in the late processing and the local extreme of PSO, an improved Particle Swarm Optimization (i-PSO) algorithm based on granules and maximum distances is proposed. By adopting tactics of linearly decreasing weight and random distribution, adding the extremum disturbance operator, and optimizing the individual extremum of particles, the i-PSO algorithm can quickly converge to an optimal global solution．The i-PSO algorithm combined with the K-means algorithm can improve the poor clustering effect and instability of the K-means algorithm caused by random initial clustering center. Finally, the i-PSO and K-means algorithms are applied to the clustering. The results of simulation experiments show that this algorithm has higher accuracy, a faster convergence rate and greater stability, and can better help to realize layered teaching in College Computer Basic Application Course.
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Xu, Y., and U. Stilla. "CONTOUR EXTRACTION OF PLANAR ELEMENTS OF BUILDING FACADES FROM POINT CLOUDS USING GLOBAL GRAPH-BASED CLUSTERING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 211–19. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-211-2019.
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<p><strong>Abstract.</strong> In this work, we present a surface-based method to extract the contours of planar building elements in the urban scene. A bottom-up segmentation method that utilizes global graph-based optimization and supervoxel structure is developed, enabling an automatic and unsupervised segmentation of point clouds. Then, a planarity-based extraction is conducted to segments, and only the planar segments, as well as their neighborhoods, are selected as candidates for the plane fitting. The points of the plane can be identified by the parametric model given by the planarity calculation. Afterward, the boundary points of the extracted plane are extracted by the alpha-shape. Optionally, line segments can be fitted and optimized by the energy minimization with the local graphical model. The experimental results using different datasets reveal that our proposed segmentation methods can be effective and comparable with other method, and the contours of planar building elements can be well extracted from the complex urban scene.</p>
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Chen, Danyang, Xiangyu Wang, Xiu Xu, Cheng Zhong, and Jinhui Xu. "Sparse non-negative matrix factorization for uncertain data clustering." Intelligent Data Analysis 26, no. 3 (April 18, 2022): 615–36. http://dx.doi.org/10.3233/ida-205622.
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We consider the problem of clustering a set of uncertain data, where each data consists of a point-set indicating its possible locations. The objective is to identify the representative for each uncertain data and group them into k clusters so as to minimize the total clustering cost. Different from other models, our model does not assume that there is a probability distribution for each uncertain data. Thus, all possible locations need to be considered to determine the representative. Existing methods for this problem are either impractical or have difficulty to handle large-scale datasets due to their pairwise-distance based global search strategy and expensive optimization computation. In this paper, we propose a novel sparse Non-negative Matrix Factorization (NMF) method which measures the similarity of uncertain data by their most commonly shared features. A divide-and-conquer approach is adopted to remarkably improve the efficiency. A novel diagonal l0-constraint and its l1 relaxation are proposed to overcome the challenge of determining the representatives. We give a detailed analysis to show the correctness of our method, and provide an effective initialization and peeling strategy to enhance the ability of processing large-scale datasets. Experimental results on some benchmark datasets confirm the effectiveness of our method.
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Chen, Danyang, Xiangyu Wang, Xiu Xu, Cheng Zhong, and Jinhui Xu. "Sparse non-negative matrix factorization for uncertain data clustering." Intelligent Data Analysis 26, no. 3 (April 18, 2022): 615–36. http://dx.doi.org/10.3233/ida-205622.
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We consider the problem of clustering a set of uncertain data, where each data consists of a point-set indicating its possible locations. The objective is to identify the representative for each uncertain data and group them into k clusters so as to minimize the total clustering cost. Different from other models, our model does not assume that there is a probability distribution for each uncertain data. Thus, all possible locations need to be considered to determine the representative. Existing methods for this problem are either impractical or have difficulty to handle large-scale datasets due to their pairwise-distance based global search strategy and expensive optimization computation. In this paper, we propose a novel sparse Non-negative Matrix Factorization (NMF) method which measures the similarity of uncertain data by their most commonly shared features. A divide-and-conquer approach is adopted to remarkably improve the efficiency. A novel diagonal l0-constraint and its l1 relaxation are proposed to overcome the challenge of determining the representatives. We give a detailed analysis to show the correctness of our method, and provide an effective initialization and peeling strategy to enhance the ability of processing large-scale datasets. Experimental results on some benchmark datasets confirm the effectiveness of our method.
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Yang, Pingle, Xin Liu, and Guiqiong Xu. "An extended clustering method using H-index and minimum distance for searching multiple key spreaders." International Journal of Modern Physics C 30, no. 07 (July 2019): 1940008. http://dx.doi.org/10.1142/s0129183119400084.
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Identifying multiple key spreaders in a network can effectively control the diffusion of information and optimize the use of available resources, which can be also called the influence maximization problem in sociology domains. In order to maximize collective influence in complex networks, multiple spreaders must have both large single influence and small overlapping influence, but it is rather difficult to satisfy these two conditions simultaneously. In this paper, we try to achieve the best compromise between importance and dispersibility for multiple spreaders through clustering. The cluster centers are surrounded by nodes with lower influence, and the distance among different cluster centers is relatively far. In addition, the initial centers selection directly affects the efficiency of clustering and the realization of global optimization. Consequently, we present an initial centers selection algorithm combining H-index and minimum distance. The experimental results on four actual datasets show that the proposed method has better performance than the traditional benchmark methods in terms of transmission speed, diffusion scale and structural characteristics.
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Hong, Xijun. "Basketball Data Analysis Using Spark Framework and K-Means Algorithm." Journal of Healthcare Engineering 2021 (July 27, 2021): 1–7. http://dx.doi.org/10.1155/2021/6393560.
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With the rapid development, different information relating to sports may now be recorded forms of useful big data through wearable and sensing technology. Big data technology has become a pressing challenge to tackle in the present basketball training, which improves the effect of baseball analysis. In this study, we propose the Spark framework based on in-memory computing for big data processing. First, we use a new swarm intelligence optimization cuckoo search algorithm because the algorithm has fewer parameters, powerful global search ability, and support of fast convergence. Second, we apply the traditional K-clustering algorithm to improve the final output using clustering means in Spark distributed environment. Last, we examine the aspects that could lead to high-pressure game circumstances to study professional athletes’ defensive performance. Both recruiters and trainers may use our technique to better understand essential player’s qualities and eventually, to assess and improve a team’s performance. The experimental findings reveal that the suggested approach outperforms previous methods in terms of clustering performance and practical utility. It has the greatest influence on the shooting training impact when moving, yielding complimentary outcomes in the training effect.
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Ma, Yan, Caihong Ma, Peng Liu, Jin Yang, Yuzhu Wang, Yueqin Zhu, and Xiaoping Du. "Spatial-Temporal Distribution Analysis of Industrial Heat Sources in the US with Geocoded, Tree-Based, Large-Scale Clustering." Remote Sensing 12, no. 18 (September 19, 2020): 3069. http://dx.doi.org/10.3390/rs12183069.
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Heavy industrial burning contributes significantly to the greenhouse gas (GHG) emissions. It is responsible for almost one-quarter of the global energy-related CO2 emissions and its share continues to grow. Mostly, those industrial emissions are accompanied by a great deal of high-temperature heat emissions from the combustion of carbon-based fuels by steel, petrochemical, or cement plants. Fortunately, these industrial heat emission sources treated as thermal anomalies can be detected by satellite-borne sensors in a quantitive way. However, most of the dominant remote sensing-based fire detection methods barely work well for heavy industrial heat source discernment. Although the object-oriented approach, especially the data clustering-based approach, has guided a novel method of detection, it is still limited by the costly computation and storage resources. Furthermore, when scaling to a national, or even global, long time-series detection, it is greatly challenged by the tremendous computation introduced by the incredible large-scale data clustering of tens of millions of high-dimensional fire data points. Therefore, we proposed an improved parallel identification method with geocoded, task-tree-based, large-scale clustering for the spatial-temporal distribution analysis of industrial heat emitters across the United States from long time-series active Visible Infrared Imaging Radiometer Suite (VIIRS) data. A recursive k-means clustering method is introduced to gradually segment and cluster industrial heat objects. Furthermore, in order to avoid the blindness caused by random cluster center initialization, the time series VIIRS hotspots data are spatially pre-grouped into GeoSOT-encoded grid tasks which are also treated as initial clustering objects. In addition, some grouped parallel clustering strategy together with geocoding-aware task tree scheduling is adopted to sufficiently exploit parallelism and performance optimization. Then, the spatial-temporal distribution pattern and its changing trend of industrial heat emitters across the United States are analyzed with the identified industrial heat sources. Eventually, the performance experiment also demonstrated the efficiency and encouraging scalability of this approach.