Journal articles on the topic 'Cluster clustering'
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Ahamad, Mohammed Gulam, Mohammed Faisal Ahmed, and Mohammed Yousuf Uddin. "Clustering as Data Mining Technique in Risk Factors Analysis of Diabetes, Hypertension and Obesity." European Journal of Engineering and Technology Research 1, no. 6 (July 27, 2018): 88–93. http://dx.doi.org/10.24018/ejeng.2016.1.6.202.
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This investigation explores data mining using open source software WEKA in health care application. The cluster analysis technique is utilized to study the effects of diabetes, obesity and hypertension from the database obtained from Virginia school of Medicine. The simple k-means cluster techniques are adopted to form ten clusters which are clearly discernible to distinguish the differences among the risk factors such as diabetes, obesity and hypertension. Cluster formation was tried by trial and error method and also kept the SSE as low as possible. The SSE is low when numbers of clusters are more. Less than ten clusters formation unable to yield distinguishable information. In this work each cluster is revealing quit important information about the diabetes, obesity, hypertension and their interrelation. Cluster 0: Diabetes ? Obesity ? Hypertension = Healthy patient, Cluster 1: Diabetes ? Obesity ? Hypertension = Healthy patient, Cluster2: Diabetes ? Obesity ? Hypertension = Obesity, Cluster3: Diabetes ? Obesity ? Hypertension = Patients with Obesity and Hypertension, Cluster4: Boarder line Diabetes ? Obesity ? Hypertension = Sever obesity, Cluster5: Obesity ? Hyper tension ? Diabetes = Hypertension, Cluster6: Border line obese ? Border line hypertension ? Diabetes = No serious complications, Cluster 7: Obesity ? Hypertension ? Diabetes= Healthy patients, Cluster 8: Obesity ? Hypertension ? Diabetes= Healthy patients, and Cluster 9: Diabetes ? Hyper tension ? Obesity = High risk unhealthy patients.
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Barnes, J., A. Dekel, G. Efstathiou, and C. S. Frenk. "Cluster-cluster clustering." Astrophysical Journal 295 (August 1985): 368. http://dx.doi.org/10.1086/163381.
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Rosing, K. E., and C. S. ReVelle. "Optimal Clustering." Environment and Planning A: Economy and Space 18, no. 11 (November 1986): 1463–76. http://dx.doi.org/10.1068/a181463.
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Cluster analysis can be performed with several models. One method is to seek those clusters for which the total flow between all within-cluster members is a maximum. This model has, until now, been viewed as mathematically difficult because of the presence of products of integer variables in the objective function. In another optimization model of cluster analysis, the p-median, a central member is found for each cluster, so that relationships of cluster members with the various central members are maximized (or minimized). This problem, although mathematically tractable, is a less realistic formulation of the general clustering problem. The formulation of the maximum interflow problem is here transformed in stages into a linear analogue which is economically solvable. Computation experience with the several transformed stages is reported and a practical example of the analysis demonstrated.
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Popkov, Yuri S., Yuri A. Dubnov, and Alexey Yu Popkov. "Entropy-Randomized Clustering." Mathematics 10, no. 19 (October 10, 2022): 3710. http://dx.doi.org/10.3390/math10193710.
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This paper proposes a clustering method based on a randomized representation of an ensemble of possible clusters with a probability distribution. The concept of a cluster indicator is introduced as the average distance between the objects included in the cluster. The indicators averaged over the entire ensemble are considered the latter’s characteristics. The optimal distribution of clusters is determined using the randomized machine learning approach: an entropy functional is maximized with respect to the probability distribution subject to constraints imposed on the averaged indicator of the cluster ensemble. The resulting entropy-optimal cluster corresponds to the maximum of the optimal probability distribution. This method is developed for binary clustering as a basic procedure. Its extension to t-ary clustering is considered. Some illustrative examples of entropy-randomized clustering are given.
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Baisya, Ritasman, Phani Kumar Devarasetti, Murthy G. S. R., and Liza Rajasekhar. "Autoantibody Clustering in Systemic Lupus Erythematosus–Associated Pulmonary Arterial Hypertension." Indian Journal of Cardiovascular Disease in Women - WINCARS 06, no. 02 (April 2021): 100–105. http://dx.doi.org/10.1055/s-0041-1732510.
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AbstractSystemic lupus erythematous–associated pulmonary arterial hypertension (SLE-PAH) is one of the important causes of mortality in lupus patients. Different autoantibodies are associated with SLE-PAH which can predict its future development. The objective of the study was to identify distinct autoantibody-based clusters in SLE-PAH patients and to compare demographic characters, clinical phenotypes, and therapeutic strategy across the clusters. Three distinct autoantibody clusters were identified using k-means cluster analysis in 71 SLE-PAH patients. Cluster1 had predominant Sm-RNP, Smith, SS-A association; cluster 2 had no definite autoantibody association; and cluster 3 was associated with nucleosome, histone, dsDNA, and ribosomal P protein. Patients in cluster 3 had a highly active disease while those in cluster 1 had significant cytopenia. Mean age and mean right ventricular systolic pressure (RVSP) were both high in cluster 2, indicating later-onset PAH in this group. This was the first autoantibody-based cluster analysis study in SLE-PAH patients in India which confirmed that autoantibodies did exist as clusters and the presence of definite autoantibodies can predict future development of pulmonary hypertension in these patients.
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Alfian, Muhammad, Ali Ridho Barakbah, and Idris Winarno. "Indonesian Online News Extraction and Clustering Using Evolving Clustering." JOIV : International Journal on Informatics Visualization 5, no. 3 (September 23, 2021): 280. http://dx.doi.org/10.30630/joiv.5.3.537.
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43,000 online media outlets in Indonesia publish at least one to two stories every hour. The amount of information exceeds human processing capacity, resulting in several impacts for humans, such as confusion and psychological pressure. This study proposes the Evolving Clustering method that continually adapts existing model knowledge in the real, ever-evolving environment without re-clustering the data. This study also proposes feature extraction with vector space-based stemming features to improve Indonesian language stemming. The application of the system consists of seven stages, (1) Data Acquisition, (2) Data Pipeline, (3) Keyword Feature Extraction, (4) Data Aggregation, (5) Predefined Cluster using Automatic Clustering algorithm, (6) Evolving Clustering, and (7) News Clustering Result. The experimental results show that Automatic Clustering generated 388 clusters as predefined clusters from 3.000 news. One of them is the unknown cluster. Evolving clustering runs for two days to cluster the news by streaming, resulting in a total of 611 clusters. Evolving clustering goes well, both updating models and adding models. The performance of the Evolving Clustering algorithm is quite good, as evidenced by the cluster accuracy value of 88%. However, some clusters are not right. It should be re-evaluated in the keyword feature extraction process to extract the appropriate features for grouping. In the future, this method can be developed further by adding other functions, updating and adding to the model, and evaluating.
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Cornell, John E., Jacqueline A. Pugh, John W. Williams, Jr, Lewis Kazis, Austin F. S. Lee, Michael L. Parchman, John Zeber, Thomas Pederson, Kelly A. Montgomery, and Polly Hitchcock Noël. "Multimorbidity Clusters: Clustering Binary Data From Multimorbidity Clusters: Clustering Binary Data From a Large Administrative Medical Database." Applied Multivariate Research 12, no. 3 (January 13, 2009): 163. http://dx.doi.org/10.22329/amr.v12i3.658.
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Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Multimorbidity is the co-occurrence of 2 or more illnesses within a single person, which raises the question whether consistent, clinically useful multimorbidity groups exist among sets of chronic illnesses. Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Application of cluster analysis involves a sequence of critical methodological and analytic decisions that influence the quality and meaning of the clusters produced. We illustrate the application of cluster analysis to identify multimorbidity clusters in a set of 45 chronic illnesses in primary care patients (N = 1,327,328), with 2 or more chronic conditions, served by the Veterans Health Administration. Six clinically useful multimorbidity clusters were identified: a Metabolic Cluster, an Obesity Cluster, a Liver Cluster, a Neurovascular Cluster, a Stress Cluster and a Dual Diagnosis Cluster. Cluster analysis appears to be a useful technique for identifying multiple disease clusters and patterns of multimorbidity.
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Li, Hong-Dong, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn, and Jianxin Wang. "ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets." Journal of Bioinformatics and Computational Biology 18, no. 03 (June 2020): 2040009. http://dx.doi.org/10.1142/s0219720020400090.
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Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php
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BORGELT, CHRISTIAN. "RESAMPLING FOR FUZZY CLUSTERING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 05 (October 2007): 595–614. http://dx.doi.org/10.1142/s0218488507004893.
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Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster structures. In this paper I give an overview how such resampling approaches can be transferred to fuzzy and probabilistic clustering. I study several cluster comparison measures, which can be parameterized with t-norms, and report experiments that provide some guidance which of them may be the best choice.
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Miyamoto, Sadaaki, Youhei Kuroda, and Kenta Arai. "Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (September 20, 2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.
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In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.
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Niu, Huan, Nasim Khozouie, Hamid Parvin, Hamid Alinejad-Rokny, Amin Beheshti, and Mohammad Reza Mahmoudi. "An Ensemble of Locally Reliable Cluster Solutions." Applied Sciences 10, no. 5 (March 10, 2020): 1891. http://dx.doi.org/10.3390/app10051891.
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Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic decisions are better than dictatorial decisions, it seems clear and simple that ensemble (here, clustering ensemble) decisions are better than simple model (here, clustering) decisions. But it is not guaranteed that every ensemble is better than a simple model. An ensemble is considered to be a better ensemble if their members are valid or high-quality and if they participate according to their qualities in constructing consensus clustering. In this paper, we propose a clustering ensemble framework that uses a simple clustering algorithm based on kmedoids clustering algorithm. Our simple clustering algorithm guarantees that the discovered clusters are valid. From another point, it is also guaranteed that our clustering ensemble framework uses a mechanism to make use of each discovered cluster according to its quality. To do this mechanism an auxiliary ensemble named reference set is created by running several kmeans clustering algorithms.
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Alaqtash, Mohammad, Moayad A.Fadhil, and Ali F. Al-Azzawi. "A Modified Overlapping Partitioning Clustering Algorithm for Categorical Data Clustering." Bulletin of Electrical Engineering and Informatics 7, no. 1 (March 1, 2018): 55–62. http://dx.doi.org/10.11591/eei.v7i1.896.
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Clustering is one of the important approaches for Clustering enables the grouping of unlabeled data by partitioning data into clusters with similar patterns. Over the past decades, many clustering algorithms have been developed for various clustering problems. An overlapping partitioning clustering (OPC) algorithm can only handle numerical data. Hence, novel clustering algorithms have been studied extensively to overcome this issue. By increasing the number of objects belonging to one cluster and distance between cluster centers, the study aimed to cluster the textual data type without losing the main functions. The proposed study herein included over twenty newsgroup dataset, which consisted of approximately 20000 textual documents. By introducing some modifications to the traditional algorithm, an acceptable level of homogeneity and completeness of clusters were generated. Modifications were performed on the pre-processing phase and data representation, along with the number methods which influence the primary function of the algorithm. Subsequently, the results were evaluated and compared with the k-means algorithm of the training and test datasets. The results indicated that the modified algorithm could successfully handle the categorical data and produce satisfactory clusters.
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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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Gao, Yu, and Yi-peng Jing. "Clustering of near clusters versus cluster compactness." Monthly Notices of the Royal Astronomical Society 236, no. 3 (February 1989): 559–65. http://dx.doi.org/10.1093/mnras/236.3.559.
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Huang, Lei, and Chan Le Wu. "Clustering Based on NMTF Algorithm." Advanced Materials Research 718-720 (July 2013): 2365–69. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2365.
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NMTF(Normalizing Mapping Training Framework) operates by mapping initial cluster centers and then iteratively training points to clusters base on the proximate cluster center and updating cluster centers. we regard finding good cluster centers as a normalizing parameter estimation problem then constructing the parameters of other normalizing models yields a space of novel clustering methods. In this paper we propose the idea using abstract of a text to members of a data partition in place of estimation of cluster centers. The method can accurately reconstruct meaning representation group used to generate a given data set.
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Hagoel, Lea, Liora Ore, Efrat Neter, Zmira Silman, and Gad Rennert. "Clustering Women’s Health Behaviors." Health Education & Behavior 29, no. 2 (April 2002): 170–82. http://dx.doi.org/10.1177/109019810202900203.
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This study attempts to characterize health lifestyles by subgrouping women with similar behavior patterns. Data on background, health behaviors, and perceptions were collected via phone interview from 1,075 Israeli women aged 50 to 74. From a cluster analysis conducted on health behaviors, three clusters emerged: a “health promoting” cluster (44.1%), women adhering to recommended behaviors; an “inactive” cluster (40.3%), women engaging in neither health-promoting nor compromising behaviors; and an “ambivalent” cluster (15.4%), women engaging somewhat in both health-promoting and compromising behaviors. Clustering was cross-tabulated by demographic and perceptual variables, further validating the subgrouping. The cluster solution was also validated by predicting another health behavior (mammography screening) for which there was an external validating source. Findings are discussed in comparison to published cluster solutions, culminating in suggestions for intervention alternatives. The concept of lifestyle was deemed appropriate to summarize the clustering of these behavioral, perceptual, and structural variables.
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Mojarad, Musa, Hamid Parvin, Samad Nejatian, and Vahideh Rezaie. "Consensus Function Based on Clusters Clustering and Iterative Fusion of Base Clusters." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 01 (February 2019): 97–120. http://dx.doi.org/10.1142/s0218488519500053.
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In clustering ensemble, it is desired to combine several clustering outputs in order to create better results than the output results of the basic individual clustering methods in terms of consistency, robustness and performance. In this research, we want to present a clustering ensemble method with a new aggregation function. The proposed method is named Robust Clustering Ensemble based on Iterative Fusion of Base Clusters (RCEIFBC). This method takes into account the two similarity criteria: (a) one of them is the cluster-cluster similarity and (b) the other one is the object-cluster similarity. The proposed method has two steps and has been done on the binary cluster representation of the given ensemble. Indeed, before doing any step, the primary partitions are converted into a binary cluster representation where the primary ensemble has been broken into a number of primary binary clusters. The first step is to combine the primary binary clusters with the highest cluster-cluster similarity. This phase will be replicated as long as our desired candidate clusters are ready. The second step is to improve the merged clusters by assigning the data points to the merged clusters. The performance and robustness of the proposed method have been evaluated over different machine learning datasets. The experimentation indicates the effectiveness of the proposed method comparing to the state-of-the-art clustering methods in terms of performance and robustness.
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Feng, Xue Bo, Fang Yao, Zhi Gang Li, and Xiao Jing Yang. "Improved Fuzzy C-Means Based on the Optimal Number of Clusters." Applied Mechanics and Materials 392 (September 2013): 803–7. http://dx.doi.org/10.4028/www.scientific.net/amm.392.803.
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According to the number of cluster centers, initial cluster centers, fuzzy factor, iterations and threshold, Fuzzy C-means clustering algorithm (FCM) clusters the data set. FCM will encounter the initialization problem of clustering prototype. Firstly, the article combines the maximum and minimum distance algorithm and K-means algorithm to determine the number of clusters and the initial cluster centers. Secondly, the article determines the optimal number of clusters with Silhouette indicators. Finally, the article improves the convergence rate of FCM by revising membership constantly. The improved FCM has good clustering effect, enhances the optimized capability, and improves the efficiency and effectiveness of the clustering. It has better tightness in the class, scatter among classes and cluster stability and faster convergence rate than the traditional FCM clustering method.
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Malshetty*, Gajendran, and Dr Basavaraj Mathapati. "WSN Clustering Based on EECI (Energy Efficient Clustering using Interconnection) Method." International Journal of Innovative Technology and Exploring Engineering 9, no. 1 (November 30, 2019): 3564–71. http://dx.doi.org/10.35940/ijitee.a3799.119119.
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in WSN, clustering gives an effective way to enhance the network lifetime. Moreover It has been observed that the clustering algorithm utilizes the two main technique first is selection of cluster head and cycling it periodically in order to distribute the energy among the clusters and this in terms increases the lifetime of network. Another challenge comes with this is minimize the energy consumption. In past several algorithm has been proposed to increase the lifetime of the network and energy consumption, however these methodologies lacks from efficiency. In this paper, we have proposed a methodologies named as EE-CI (Energy Efficient Clustering using Interconnection), along with the random updation. Here the networks are parted into different clusters, the cluster updation are done based on the CHC scheme. Moreover, in proposed methodology cluster updation and data sample is determined through the change in sensor data. Here we propose a method for sampling sensor and CHC for selecting the cluster head to balance the energy and improvise the energy efficiency. Moreover, the proposed methodology is evaluated and the result is demonstrated by considering the Leach as existing methodology, experiments results shows that the proposed methodology outperforms the existing methodology.
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Honda, Katsuhiro, Nami Yamamoto, Seiki Ubukata, and Akira Notsu. "Noise Rejection in MMMs-Induced Fuzzy Co-Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 7 (November 20, 2017): 1144–51. http://dx.doi.org/10.20965/jaciii.2017.p1144.
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Noise rejection is an important issue in practical application of FCM-type fuzzy clustering, and noise clustering achieves robust estimation of cluster prototypes with an additional noise cluster for dumping noise objects into it. Noise objects having larger distances from all clusters are designed to be assigned to the noise cluster, which is located in an equal (fixed) distance from all objects. Fuzzy co-clustering is an extended version of FCM-type clustering for handling cooccurrence information among objects and items, where the goal of analysis is to extract pair-wise clusters of familiar objects and items. This paper proposes a novel noise rejection model for fuzzy co-clustering induced by multinomial mixture models (MMMs), where a noise cluster is defined with homogeneous item memberships for drawing noise objects having dissimilar cooccurrence features from all general clusters. The noise rejection scheme can be also utilized in selecting the optimal cluster number through a sequential implementation with different cluster numbers.
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Wong, Yiu-fai. "Clustering Data by Melting." Neural Computation 5, no. 1 (January 1993): 89–104. http://dx.doi.org/10.1162/neco.1993.5.1.89.
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We derive a new clustering algorithm based on information theory and statistical mechanics, which is the only algorithm that incorporates scale. It also introduces a new concept into clustering: cluster independence. The cluster centers correspond to the local minima of a thermodynamic free energy, which are identified as the fixed points of a one-parameter nonlinear map. The algorithm works by melting the system to produce a tree of clusters in the scale space. Melting is also insensitive to variability in cluster densities, cluster sizes, and ellipsoidal shapes and orientations. We tested the algorithm successfully on both simulated data and a Synthetic Aperture Radar image of an agricultural site with 12 attributes for crop identification.
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Carniel, Théophile, José Halloy, and Jean-Michel Dalle. "A novel clustering approach to bipartite investor-startup networks." PLOS ONE 18, no. 1 (January 5, 2023): e0279780. http://dx.doi.org/10.1371/journal.pone.0279780.
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We propose a novel similarity-based clustering approach to venture capital investors that takes as input the bipartite graph of funding interactions between investors and startups and returns clusterings of investors built upon 5 characteristic dimensions. We first validate that investors are clustered in a meaningful manner and present methods of visualizing cluster characteristics. We further analyze the temporal dynamics at the cluster level and observe a meaningful second-order evolution of the sectoral investment trends. Finally, and surprisingly, we report that clusters appear stable even when running the clustering algorithm with all but one of the 5 characteristic dimensions, for instance observing geography-focused clusters without taking into account the geographical dimension or sector-focused clusters without taking into account the sectoral dimension, suggesting the presence of significant underlying complex investment patterns.
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Wu, Jing, and Guang Xue Meng. "A New Clustering Algorithm and Relevant Theoretical Analysis for Ad-Hoc Networks." Applied Mechanics and Materials 556-562 (May 2014): 4001–4. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4001.
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In ad-hoc networks, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a new clustering algorithm (NCA) was proposed. Firstly, NCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that NCA can improve cluster stability and reduce clustering overheads.
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Hou, Guo Zhao, Jin Biao Wang, and Jing Wu. "MANET-Based Stable Clustering Algorithm and its Performance Analysis." Applied Mechanics and Materials 571-572 (June 2014): 100–104. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.100.
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In MANET, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a MANET-based stable clustering algorithm (MSCA) was proposed. Firstly, MSCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that MSCA can improve cluster stability and reduce clustering overheads.
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Setiadi, Anggun, and Erma Delima Sikumbang. "K-Means Clustering Dalam Penerimaan Karyawan Baru." INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics 4, no. 2 (June 2, 2020): 103. http://dx.doi.org/10.51211/itbi.v4i2.1304.
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Dalam penerimaan karyawan baru sulitnya bagian SDM PT. Erdikha Elit Sekuritas dalam mengelompokkan data-data karyawan baru dan tidak adanya sistem tes dalam pemilihan karyawan baru. Metode K-Means Clustering adalah salah satu metode cluster analysis non hirarki yang berusaha untuk mengelompokkan data-data yang ada satu atau lebih cluster atau kelompok, oleh karena itu metode ini sangat cocok digunakan untuk mengatasi permasalahan dalam mengelompokkan data-data calon karyawan baru dan mengimplementasikan menggunakan software RapidMiner dengan hasil penelitian 0,125% untuk cluster 1 yang berjumlah 2 data karyawan baru, 0,125% untuk cluster 2 yang berjumlah 2 data karyawan baru, dan 0,750% untuk cluster 3 yang berjumlah 12 data karyawan baru. Strategi pemilihan karyawan baru nantinya akan mengikuti cluster yang terbentuk berdasarkan data yang paling banyak diantara 3 cluster yang ada, yaitu di cluster ke- 3, karena dengan data cluster yang paling banyaklah yang lebih banyak memenuhi kriteria.
Kata kunci: K-Means Clustering, Penerimaan Karyawan Baru
Abstract: In the case of hiring new employees, the difficulty of the HR department of PT. Erdikha Elit Sekuritas in classifying new employee data and the absence of a test system in the selection of new employees. K-Means Clustering method is a non-hierarchical cluster analysis method that seeks to group existing data into one or more clusters or groups, therefore this method is very suitable to be used to overcome problems in grouping data on prospective new employees and implements using RapidMiner software with research results of 0.125% for cluster 1 which amounts to 2 new employee data, 0.125% for cluster 2 which amounts to 2 new employee data, and 0.750% for cluster 3 which amounts to 12 new employee data. The new employee selection strategy will follow the clusters formed based on the most data among the 3 existing clusters, namely in the 3rd cluster, because with the most data clusters that meet more the required criteria.
Keywords: Acceptance of new employees, K-Means Clustering.
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Wang, Fu Yan, Sha Qiu, and Qing Li. "Complex Opinion Network Correlation Clustering." Applied Mechanics and Materials 644-650 (September 2014): 2846–49. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2846.
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In this paper, 2112 specific correlation data of 2 types cluster were selected as sample to build a weighted network, including each hour sample is represented by a vertex and a correlation between 2 clusters is represented by an edge. We analysis this network structure by complex network theory and computer method. We found that the correlation clusters of 2 media have an important impact on this complex network, and the specific sample follow a frequency distribution of the weighted degrees. Applying the method of k-core shows small groups in this complex network, also the modularity calculating help us find out the key cluster, the correlation cluster, the medium cluster and the interaction path of them. An apparently small-world effect has found by the shortest path calculating effectively. All of these may provide a scientific and reasonable reference for further research.
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Liu, Jing, Fuyuan Cao, Xiao-Zhi Gao, Liqin Yu, and Jiye Liang. "A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4860–67. http://dx.doi.org/10.1609/aaai.v34i04.5922.
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Clustering by jointly exploiting information from multiple views can yield better performance than clustering on one single view. Some existing multi-view clustering methods aim at learning a weight for each view to determine its contribution to the final solution. However, the view-weighted scheme can only indicate the overall importance of a view, which fails to recognize the importance of each inner cluster of a view. A view with higher weight cannot guarantee all clusters in this view have higher importance than them in other views. In this paper, we propose a cluster-weighted kernel k-means method for multi-view clustering. Each inner cluster of each view is assigned a weight, which is learned based on the intra-cluster similarity of the cluster compared with all its corresponding clusters in different views, to make the cluster with higher intra-cluster similarity have a higher weight among the corresponding clusters. The cluster labels are learned simultaneously with the cluster weights in an alternative updating way, by minimizing the weighted sum-of-squared errors of the kernel k-means. Compared with the view-weighted scheme, the cluster-weighted scheme enhances the interpretability for the clustering results. Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed method.
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Lin, Jun-Lin, Jen-Chieh Kuo, and Hsing-Wang Chuang. "Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering." Symmetry 12, no. 7 (July 14, 2020): 1168. http://dx.doi.org/10.3390/sym12071168.
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Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.
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Kurniasari, Ratna, Rukun Santoso, and Alan Prahutama. "ANALISIS KECENDERUNGAN LAPORAN MASYARAKAT PADA “LAPORGUB..!” PROVINSI JAWA TENGAH MENGGUNAKAN TEXT MINING DENGAN FUZZY C-MEANS CLUSTERING." Jurnal Gaussian 10, no. 4 (December 31, 2021): 544–53. http://dx.doi.org/10.14710/j.gauss.v10i4.33101.
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Effective communication between the government and society is essential to achieve good governance. The government makes an effort to provide a means of public complaints through an online aspiration and complaint service called “LaporGub..!”. To group incoming reports easier, the topic of the report is searched by using clustering. Text Mining is used to convert text data into numeric data so that it can be processed further. Clustering is classified as soft clustering (fuzzy) and hard clustering. Hard clustering will divide data into clusters strictly without any overlapping membership with other clusters. Soft clustering can enter data into several clusters with a certain degree of membership value. Different membership values make fuzzy grouping have more natural results than hard clustering because objects at the boundary between several classes are not forced to fully fit into one class but each object is assigned a degree of membership. Fuzzy c-means has an advantage in terms of having a more precise placement of the cluster center compared to other cluster methods, by improving the cluster center repeatedly. The formation of the best number of clusters is seen based on the maximum silhouette coefficient. Wordcloud is used to determine the dominant topic in each cluster. Word cloud is a form of text data visualization. The results show that the maximum silhouette coefficient value for fuzzy c-means clustering is shown by the three clusters. The first cluster produces a word cloud regarding road conditions as many as 449 reports, the second cluster produces a word cloud regarding covid assistance as many as 964 reports, and the third cluster produces a word cloud regarding farmers fertilizers as many as 176 reports. The topic of the report regarding covid assistance is the cluster with the most number of members.
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Li, Ning, Yunxia Gu, and Zhongliang Deng. "Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5707692.
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A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.
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Hofmans, Joeri, Tim Vantilborgh, and Omar N. Solinger. "k-Centres Functional Clustering." Organizational Research Methods 21, no. 4 (August 17, 2017): 905–30. http://dx.doi.org/10.1177/1094428117725793.
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In the present paper, we introduce k-centres functional clustering ( k-centres FC), a person-centered method that clusters people with similar patterns of complex, highly nonlinear change over time. We review fundamentals of the methodology and argue how it addresses some of the limitations of the traditional approaches to modeling repeated measures data. The usefulness of k-centres FC is demonstrated by applying the method to weekly measured commitment data from 109 participants who reported psychological contract breach events. The k-centres FC analysis shows two substantively meaningful clusters, the first cluster showing reaction patterns with general growth in commitment after breach and the second cluster showing general decline in commitment after breach. Further, the reaction patterns in the second cluster appear to be the result of a combination of two interesting reaction logics: immediate and delayed reactions. We conclude by outlining how future organizational research can incorporate this methodology.
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Asrriningtias, Salnan Ratih. "Cluster Validity Index to Determine the Optimal Number Clusters of Fuzzy Clustering for Classify Customer Buying Behavior." Journal of Development Research 5, no. 1 (May 31, 2021): 7–12. http://dx.doi.org/10.28926/jdr.v5i1.134.
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One of the strategies in order to compete in Batik MSMEs is to look at the characteristics of the customer. To make it easier to see the characteristics of customer buying behavior, it is necessary to classify customers based on similarity of characteristics using fuzzy clustering. One of the parameters that must be determined at the beginning of the fuzzy clustering method is the number of clusters. Increasing the number of clusters does not guarantee the best performance, but the right number of clusters greatly affects the performance of fuzzy clustering. So to get optimal number cluster, we can measured the result of clustering in each number cluster using the cluster validity index. From several types of cluster validity index, NPC give the best value. Optimal number cluster that obtained by the validity index is 2 and this number cluster give classify result with small variance value
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Endo, Yasunori, Ayako Heki, and Yukihiro Hamasuna. "Non Metric Model Based on Rough Set Representation." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (July 20, 2013): 540–51. http://dx.doi.org/10.20965/jaciii.2013.p0540.
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The non metricmodel is a kind of clustering method in which belongingness or the membership grade of each object in each cluster is calculated directly from dissimilarities between objects and in which cluster centers are not used. The clustering field has recently begun to focus on rough set representation instead of fuzzy set representation. Conventional clustering algorithms classify a set of objects into clusters with clear boundaries, that is, one object must belong to one cluster. Many objects in the real world, however, belong to more than one cluster because cluster boundaries overlap each other. Fuzzy set representation of clusters makes it possible for each object to belong to more than one cluster. The fuzzy degree of membership may, however, be too descriptive for interpreting clustering results. Rough set representation handles such cases. Clustering based on rough sets could provide a solution that is less restrictive than conventional clustering and more descriptive than fuzzy clustering. This paper covers two types of Rough-set-based Non Metric model (RNM). One algorithm is the Roughset-based Hard Non Metric model (RHNM) and the other is the Rough-set-based Fuzzy Non Metric model (RFNM). In both algorithms, clusters are represented by rough sets and each cluster consists of lower and upper approximation. The effectiveness of proposed algorithms is evaluated through numerical examples.
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Ramdany, Kevry, and Jerry Heikal. "KLASTERISASI MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING DENGAN MENGGUNAKAN INDIKATOR TOTAL DEBT TO TOTAL EQUITY RATIO, CURRENT RATIO DAN RETURN ON ASSETS UNTUK MENGETAHUI DAMPAK COVID-19 PADA 10 INDUSTRI DI BURSA EFEK INDONESIA." Ensiklopedia of Journal 4, no. 2 (January 11, 2022): 48–53. http://dx.doi.org/10.33559/eoj.v4i2.1013.
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Clustering data is a data grouping method. This is a part of Data Mining, which is pattern extraction that interested of the big data. Clustering usually use in business intelligence, image pattern recognition, web search, field of biological sciences, and for security. Clustering is a process grouping data into clusters so that data in one cluster have the same maximum. Object in one cluster have the same characteristic between each others and different with other cluster. This research using K-means Clustering to grouping company in some industries based on available data in yahoo finance. Grouping aim to know about which good company to invest in during this pandemic. Data source financial statement in 2020 that available in yahoo finance. Analysis method using K-means clustering with software SPSS version 20. The result is 3 clusters, which is cluster 1 severely affected by covid, cluster 2 mildly affected by covid and cluster 3 moderately affected by covid. Result of analysis there is 7 companies in cluster 1, 1 companies in cluster 2, and 2 companies in cluster 3. Based on analysis result using K-means Clustering then suggested to invest on company that include in cluster 2 is Unilever Indonesia Tbk.
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Wibowo, Rizki Agung, Khoirin Nisa, Hilda Venelia, and Warsono Warsono. "ROBUST CLUSTERING OF COVID-19 PANDEMIC WORLDWIDE." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 2 (June 1, 2022): 687–94. http://dx.doi.org/10.30598/barekengvol16iss2pp687-694.
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COVID-19 pandemic is described as the most challenging crisis that humans have faced since World War II. From December 2019 until August 2021 based on the dataset provided by WHO, globally 219 countries in the world are affected by this virus. There are 205.338.159 cases cumulative total and 4.333.094 death cumulative total caused by this virus. In this paper, the data of 219 countries are analyzed using a robust clustering method namely K-Medoids cluster analysis. Based on the result, 219 countries in the world can be divided into five clusters based on four COVID-19-related variables, i.e. the number of cases cumulative total, death cumulative total, positive cases per capita, and case fatality rate. The distribution of the countries in five clusters was as follows; the first cluster contained 48 countries, the second cluster contained 3 countries, the third and fourth clusters contained 16 and 89 countries respectively, and the last cluster contained 63 countries. The largest cluster is the fourth one, containing countries that form a cluster with a centroid below the world average, and the smallest cluster is the second cluster with the high cases in all attributes, consisting of the USA, India, and Brazil.
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Hamasuna, Yukihiro, Shusuke Nakano, Ryo Ozaki, and and Yasunori Endo. "Cluster Validity Measures Based Agglomerative Hierarchical Clustering for Network Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (May 20, 2019): 577–83. http://dx.doi.org/10.20965/jaciii.2019.p0577.
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The Louvain method is a method of agglomerative hierarchical clustering (AHC) that uses modularity as the merging criterion. Modularity is an evaluation measure for network partitions. Cluster validity measures are also used to evaluate cluster partitions and to determine the optimal number of clusters. Several cluster validity measures are constructed considering the geometric features of clusters. These measures and modularity are considered to be the same concept in the viewpoint of evaluating cluster partitions. In this paper, cluster validity measures based agglomerative hierarchical clustering (CVAHC) is proposed as a novel clustering method for network data. The cluster validity measures are used as a merging criterion and an evaluation measure for network data in the proposed method. Numerical experiments show that Dunn’s and Xie-Beni’s indices for network partitions are useful for network clustering.
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Qomariyah and Maria Ulfah Siregar. "Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering." JISKA (Jurnal Informatika Sunan Kalijaga) 7, no. 2 (May 25, 2022): 91–99. http://dx.doi.org/10.14421/jiska.2022.7.2.91-99.
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Universities as educational institutions have very large amounts of academic data which may not be used properly. The data needs to be analyzed to produce information that can map the distribution of students. Student academic data processing utilizes data mining processes using clustering techniques, K-Means and K-Medoids. This study aims to implement and analyze the comparison of which algorithm is more optimal based on the cluster validation test with the Davies Bouldin Index. The data used are academic data of UIN Sunan Kalijaga students in the 2013-2015 batch. In the k-Means process, the best number of clusters is 5 with a DBI value of 0.781. In the k-Medoids process, the best number of clusters is 3 with a DBI value of 0.929. Based on the value of the DBI validation test, the k-Means algorithm is more optimal than the k-Medoids. So that the cluster of students with the highest average GPA of 3,325 is 401 students.
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Xu, Jiaxuan, Jiang Wu, Taiyong Li, and Yang Nan. "Divergence-Based Locally Weighted Ensemble Clustering with Dictionary Learning and the L2,1-Norm." Entropy 24, no. 10 (September 21, 2022): 1324. http://dx.doi.org/10.3390/e24101324.
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Accurate clustering is a challenging task with unlabeled data. Ensemble clustering aims to combine sets of base clusterings to obtain a better and more stable clustering and has shown its ability to improve clustering accuracy. Dense representation ensemble clustering (DREC) and entropy-based locally weighted ensemble clustering (ELWEC) are two typical methods for ensemble clustering. However, DREC treats each microcluster equally and hence, ignores the differences between each microcluster, while ELWEC conducts clustering on clusters rather than microclusters and ignores the sample–cluster relationship. To address these issues, a divergence-based locally weighted ensemble clustering with dictionary learning (DLWECDL) is proposed in this paper. Specifically, the DLWECDL consists of four phases. First, the clusters from the base clustering are used to generate microclusters. Second, a Kullback–Leibler divergence-based ensemble-driven cluster index is used to measure the weight of each microcluster. With these weights, an ensemble clustering algorithm with dictionary learning and the L2,1-norm is employed in the third phase. Meanwhile, the objective function is resolved by optimizing four subproblems and a similarity matrix is learned. Finally, a normalized cut (Ncut) is used to partition the similarity matrix and the ensemble clustering results are obtained. In this study, the proposed DLWECDL was validated on 20 widely used datasets and compared to some other state-of-the-art ensemble clustering methods. The experimental results demonstrated that the proposed DLWECDL is a very promising method for ensemble clustering.
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Zhang, Xianchao, and Xiaotong Zhang. "Smart Multi-Task Bregman Clustering and Multi-Task Kernel Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 1034–40. http://dx.doi.org/10.1609/aaai.v27i1.8557.
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Multitask Bregman Clustering (MBC) alternatively updates clusters and learns relationship between clusters of different tasks, and the two phases boost each other. However, the boosting does not always have positive effect, it may also cause negative effect. Another issue of MBC is that it cannot deal with nonlinear separable data. In this paper, we show that MBC's process of using cluster relationship to boost the updating clusters phase may cause negative effect, i.e., cluster centroid may be skewed under some conditions. We propose a smart multi-task Bregman clustering (S-MBC) algorithm which identifies negative effect of the boosting and avoids the negative effect if it occurs. We then extend the framework of S-MBC to a smart multi-task kernel clustering (S-MKC) framework to deal with nonlinear separable data. We also propose a specific implementation of the framework which could be applied to any Mercer kernel. Experimental results confirm our analysis, and demonstrate the superiority of our proposed methods.
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Blanza, Jojo. "Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering." Baghdad Science Journal 18, no. 2(Suppl.) (June 20, 2021): 1001. http://dx.doi.org/10.21123/bsj.2021.18.2(suppl.).1001.
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This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The multipaths generated by C2CM were transformed using the directional cosine transform (DCT) and the whitening transform (WT). The transformed dataset was clustered using SC and 3CAM-SC. The clustering performance was validated using the Jaccard index by comparing the reference multipath dataset with the calculated multipath clusters. The results show that the effectiveness of SC is similar to the state-of-the-art clustering approaches. However, 3CAM-SC outperforms SC in all channel scenarios. SC can be used in indoor scenarios based on accuracy, while 3CAM-SC is applicable in indoor and semi-urban scenarios. Thus, the clustering approaches can be applied as alternative clustering techniques in the field of channel modeling.
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Lisangan, Erick Alfons, Aina Musdholifah, and Sri Hartati. "Two Level Clustering for Quality Improvement using Fuzzy Subtractive Clustering and Self-Organizing Map." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 373. http://dx.doi.org/10.11591/tijee.v15i2.1552.
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Recently, clustering algorithms combined conventional methods and artificial intelligence. FSC-SOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster centers are processed twice i.e. searching for data with high density at FSC then updating the cluster centers at SOM. FSC-SOM was tested using 10 datasets that is measured with F-Measure, entropy, Silhouette Index, and Dunn Index. The result showed that FSC-SOM can improve the cluster center of FSC with SOM in order to obtain the better quality of clustering results. The clustering result of FSC-SOM is better than or equal to the clustering result of FSC that proven by the value of external and internal validity measurement.
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Wada, Yuichiro, Shugo Miyamoto, Takumi Nakagama, Léo Andéol, Wataru Kumagai, and Takafumi Kanamori. "Spectral Embedded Deep Clustering." Entropy 21, no. 8 (August 15, 2019): 795. http://dx.doi.org/10.3390/e21080795.
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We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space. We use a conditional discrete probability distribution defined by a deep neural network as a statistical model. Our strategy is first to estimate the cluster labels of unlabeled data points selected from a high-density region, and then to conduct semi-supervised learning to train the model by using the estimated cluster labels and the remaining unlabeled data points. Lastly, by using the trained model, we obtain the estimated cluster labels of all given unlabeled data points. The advantage of our method is that it does not require key conditions. Existing clustering methods with deep neural networks assume that the cluster balance of a given dataset is uniform. Moreover, it also can be applied to various data domains as long as the data is expressed by a feature vector. In addition, it is observed that our method is robust against outliers. Therefore, the proposed method is expected to perform, on average, better than previous methods. We conducted numerical experiments on five commonly used datasets to confirm the effectiveness of the proposed method.
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Liu, Yu Hua, Cui Xu, Ke Xu, and Jian Zhi Jin. "An Improved Clustering Method Based on Data Field." Applied Mechanics and Materials 457-458 (October 2013): 919–25. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.919.
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By analyzing the problem of k-means, we find the traditional k-means algorithm suffers from some shortcomings, such as requiring the user to give out the number of clusters k in advance, being sensitive to the initial cluster centers, being sensitive to the noise and isolated data, only being applied to the type found in globular clusters, and being easily trapped into a local solution et cetera. This improved algorithm uses the potential of data to find the center data and eliminate the noise data. It decomposes big or extended cluster into several small clusters, then merges adjacent small clusters into a big cluster using the information provided by the Safety Area. Experimental results demonstrate that the improved k-means algorithm can determine the number of clusters, distinguish irregular cluster to a certain extent, decrease the dependence on the initial cluster centers, eliminate the effects of the noise data and get a better clustering accuracy.
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Prades, José, Gonzalo Safont, Addisson Salazar, and Luis Vergara. "Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering." Remote Sensing 12, no. 21 (November 1, 2020): 3585. http://dx.doi.org/10.3390/rs12213585.
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Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.
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Bagherzadeh karimi, Nazli. "Survey on Multi Agent Energy Efficient Clustering Algorithms in Wireless Sensor Networks." Computer Engineering and Applications Journal 3, no. 3 (September 7, 2014): 172–84. http://dx.doi.org/10.18495/comengapp.v3i3.92.
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In the last few years, there are many applications for Wireless Sensor Networks (WSNs). One of the main drawbacks of these networks is the limited battery power of sensor nodes. There are many cases to reduce energy consumption in WSNs. One of them is clustering. Sensor nodes partitioned into the clusters so that one is chosen as Cluster Head (CH). Clustering and selection of the proper node as CH is very significant in reducing energy consumption and increasing network lifetime. In this paper, we have surveyed a multi agent clustering algorithms and compared on various parameters like cluster size, cluster count, clusters equality, parameters used in CHs selection, algorithm complexity, types of algorithm used in clustering, nodes location awareness, inter-cluster and intra-cluster topologies, nodes homogeneity and MAC layer communications.
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Lei, Han-Sheng, and Chin-Hua Huang. "Geographic clustering, network relationships and competitive advantage." Management Decision 52, no. 5 (June 10, 2014): 852–71. http://dx.doi.org/10.1108/md-08-2013-0426.
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Purpose – There are contradicted perspectives on relationship between geographic cluster and competitive advantage of firms in previous research. Extant research has paid extremely attention to the effect of both geographic cluster and industrial network on firms’ performance; however, little studies have delineated the relationship between geographic cluster, industrial network, and competitive advantage. The purpose of this paper is to demonstrate that firms within the same cluster that have established idiosyncratic network resources have stronger competitive advantages than firms that have not. Design/methodology/approach – An empirical study of two prominent geographic clusters from Taiwan is analyzed by structural equation modeling. Findings – The results indicate that the degree of networking does play a mediating role between geographic cluster and competitive advantage, which may help resolve the conflicting results obtained by researchers on the influence of clusters on competitive advantage. The results also find that both degree of networking and betweenness position are conducive to the pursuit of competitive advantage. Practical implications – The research shows that firms merely locate themselves in the right cluster does not guarantee they can outperform their rivals. Rather, developing of network relationship with other firms proximate to the same cluster will strengthen a firm's competitive advantages. Originality/value – In the theoretical perspective, this paper attempts to fill the gap in the links between clusters, networks, and competitive advantage by providing that the networking as a mechanism for firms in a cluster to improve their competitive advantage.
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Mohammed Jabbar, Ayad, Ku Ruhana Ku-Mahamud, and Rafid Sagban. "An improved ACS algorithm for data clustering." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1506. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1506-1515.
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<span lang="EN-GB">Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms. </span>
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Silvi, Rini. "Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia." Jurnal Matematika "MANTIK" 4, no. 1 (May 11, 2018): 22–31. http://dx.doi.org/10.15642/mantik.2018.4.1.22-31.
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Cluster analysis is a method to group data (objects) or observations based on their similarities. Objects that become members of a group have similarities among them. Cluster analyses used in this research are K-means clustering and Centroid Linkage clustering. K-means clustering, which falls under non-hierarchical cluster analysis, is a simple and easy to implement method. On the other hand, Centroid Linkage clustering, which belongs to hierarchical cluster analysis, is useful in handling outliers by preventing them skewing the cluster analysis. To keep it simple, outliers are often removed even though outliers often contain important information. HIV/AIDS is a serious challenge for global public health since HIV/AIDS is an infectious disease attacking body’s immune system that in turn lowering the ability to fight infections which in the end causing death. HIV/AIDS indicators data in Indonesia contain outliers. This research uses gap statistic to define the number of clusters based on HIV/AIDS indicators that groups Indonesia provinces into 7 clusters. By comparing Sw/Sb ratio, Centroid Linkage clustering is more homogenous than K-means clustering. Using clustering, the government shall be able to create a better policy for fighting HIV/AIDS based on the dominant indicators in each cluster.
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K, Shyam Sunder Reddy, and Shoba Bindu C. "MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction." International Journal of Engineering & Technology 7, no. 2 (March 13, 2018): 270. http://dx.doi.org/10.14419/ijet.v7i2.9051.
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Real-time data stream clustering has been widely used in many fields, and it can extract useful information from massive sets of data. Most of the existing density-based algorithms cluster the data streams based on the density within the micro-clusters. These algorithms completely omit the data density in the area between the micro-clusters and recluster the micro-clusters based on erroneous assumptions about the distribution of the data within and between the micro-clusters that lead to poor clustering results. This paper describes a novel density-based clustering algorithm for evolving data streams called MCDAStream, which clusters the data stream based on micro-cluster density and attraction between the micro-clusters. The attraction of micro-clusters characterizes the positional information of the data points in each micro-cluster. We generate better clustering results by considering both micro-cluster density and attraction of micro-clusters. The quality of the proposed algorithm is evaluated on various synthetic and real-time datasets with distinct characteristics and quality metrics.
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Huang, Jinke, Xiaoguang Fan, Xin Xiang, Min Wan, Zhenfu Zhuo, and Yongjian Yang. "A Clustering Routing Protocol for Mobile Ad Hoc Networks." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5395894.
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The dynamic topology of a mobile ad hoc network poses a real challenge in the design of hierarchical routing protocol, which combines proactive with reactive routing protocols and takes advantages of both. And as an essential technique of hierarchical routing protocol, clustering of nodes provides an efficient method of establishing a hierarchical structure in mobile ad hoc networks. In this paper, we designed a novel clustering algorithm and a corresponding hierarchical routing protocol for large-scale mobile ad hoc networks. Each cluster is composed of a cluster head, several cluster gateway nodes, several cluster guest nodes, and other cluster members. The proposed routing protocol uses proactive protocol between nodes within individual clusters and reactive protocol between clusters. Simulation results show that the proposed clustering algorithm and hierarchical routing protocol provide superior performance with several advantages over existing clustering algorithm and routing protocol, respectively.