Clustering Using a Combination of Particle Swarm Optimization and K-means
Clustering is an unsupervised kind of grouping of data points based on the similarity that exists between them. This paper applied a combination of particle swarm optimization and K-means for data clustering. The proposed approach tries to improve the performance of traditional partition clustering...
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Online Access: | https://doi.org/10.1515/jisys-2015-0099 |
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doaj-b1915419b91d4b5c8ae93291eba1cbd62021-09-06T19:40:36ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2017-07-0126345746910.1515/jisys-2015-0099Clustering Using a Combination of Particle Swarm Optimization and K-meansPatel Garvishkumar K.0Dabhi Vipul K.1Prajapati Harshadkumar B.2Department of Information Technology, Dharmsinh Desai University, Nadiad 387001, IndiaDepartment of Information Technology, Dharmsinh Desai University, Nadiad 387001, IndiaDepartment of Information Technology, Dharmsinh Desai University, Nadiad 387001, IndiaClustering is an unsupervised kind of grouping of data points based on the similarity that exists between them. This paper applied a combination of particle swarm optimization and K-means for data clustering. The proposed approach tries to improve the performance of traditional partition clustering techniques such as K-means by avoiding the initial requirement of number of clusters or centroids for clustering. The proposed approach is evaluated using various primary and real-world datasets. Moreover, this paper also presents a comparison of results produced by the proposed approach and by the K-means based on clustering validity measures such as inter- and intra-cluster distances, quantization error, silhouette index, and Dunn index. The comparison of results shows that as the size of the dataset increases, the proposed approach produces significant improvement over the K-means partition clustering technique.https://doi.org/10.1515/jisys-2015-0099partition clusteringk-meansparticle swarm optimizationclustering validity measures |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Patel Garvishkumar K. Dabhi Vipul K. Prajapati Harshadkumar B. |
spellingShingle |
Patel Garvishkumar K. Dabhi Vipul K. Prajapati Harshadkumar B. Clustering Using a Combination of Particle Swarm Optimization and K-means Journal of Intelligent Systems partition clustering k-means particle swarm optimization clustering validity measures |
author_facet |
Patel Garvishkumar K. Dabhi Vipul K. Prajapati Harshadkumar B. |
author_sort |
Patel Garvishkumar K. |
title |
Clustering Using a Combination of Particle Swarm Optimization and K-means |
title_short |
Clustering Using a Combination of Particle Swarm Optimization and K-means |
title_full |
Clustering Using a Combination of Particle Swarm Optimization and K-means |
title_fullStr |
Clustering Using a Combination of Particle Swarm Optimization and K-means |
title_full_unstemmed |
Clustering Using a Combination of Particle Swarm Optimization and K-means |
title_sort |
clustering using a combination of particle swarm optimization and k-means |
publisher |
De Gruyter |
series |
Journal of Intelligent Systems |
issn |
0334-1860 2191-026X |
publishDate |
2017-07-01 |
description |
Clustering is an unsupervised kind of grouping of data points based on the similarity that exists between them. This paper applied a combination of particle swarm optimization and K-means for data clustering. The proposed approach tries to improve the performance of traditional partition clustering techniques such as K-means by avoiding the initial requirement of number of clusters or centroids for clustering. The proposed approach is evaluated using various primary and real-world datasets. Moreover, this paper also presents a comparison of results produced by the proposed approach and by the K-means based on clustering validity measures such as inter- and intra-cluster distances, quantization error, silhouette index, and Dunn index. The comparison of results shows that as the size of the dataset increases, the proposed approach produces significant improvement over the K-means partition clustering technique. |
topic |
partition clustering k-means particle swarm optimization clustering validity measures |
url |
https://doi.org/10.1515/jisys-2015-0099 |
work_keys_str_mv |
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1717768161748582400 |