Full Citation

Title: Clustering Using a Combination of Particle Swarm Optimization and K-means

Citation Type: Journal Article

Publication Year: 2017

ISBN:

ISSN: 03341860

DOI: 10.1515/jisys-2015-0099

NSFID:

PMCID:

PMID:

Abstract: 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.

Url: https://www.degruyter.com/view/j/jisys.2017.26.issue-3/jisys-2015-0099/jisys-2015-0099.xml

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Authors: Patel, Garvishkumar K.; Dabhi, Vipul K.; Prajapati, Harshadkumar B.

Periodical (Full): Journal of Intelligent Systems

Issue: 3

Volume: 26

Pages: 457-469

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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