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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.<\/jats:p>","DOI":"10.1515\/jisys-2015-0099","type":"journal-article","created":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T04:08:10Z","timestamp":1499054890000},"page":"457-469","source":"Crossref","is-referenced-by-count":18,"title":["Clustering Using a Combination of Particle Swarm Optimization and K-means"],"prefix":"10.1515","volume":"26","author":[{"given":"Garvishkumar K.","family":"Patel","sequence":"first","affiliation":[{"name":"Department of Information Technology , Dharmsinh Desai University , Nadiad 387001 , India"}]},{"given":"Vipul K.","family":"Dabhi","sequence":"additional","affiliation":[{"name":"Department of Information Technology , Dharmsinh Desai University , Nadiad 387001 , India"}]},{"given":"Harshadkumar B.","family":"Prajapati","sequence":"additional","affiliation":[{"name":"Department of Information Technology , Dharmsinh Desai University , Nadiad 387001 , India"}]}],"member":"374","published-online":{"date-parts":[[2016,5,26]]},"reference":[{"key":"2025120523272197154_j_jisys-2015-0099_ref_001_w2aab3b7d118b1b6b1ab2ab1Aa","unstructured":"S. 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