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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/577660
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dc.contributor.authorVijay Kumar
dc.contributor.authorJitender Kumar Chhabra
dc.contributor.authorDinesh Kumar
dc.date.accessioned2023-11-06T02:46:11Z-
dc.date.available2023-11-06T02:46:11Z-
dc.date.issued2016-07
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:82892
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/577660-
dc.descriptionThe main challenges of clustering techniques are to tune the initial cluster centres and to avoid the solution being trapped in the local optima. In this paper, a new metaheuristic algorithm, Differential Search (DS), is used to solve these problems. The DS explores the search space of the given dataset to find the near-optimal cluster centres. The cluster centre-based encoding scheme is used to evolve the cluster centres. The proposed DS-based clustering technique is tested over four real-life datasets. The performance of DS-based clustering is compared with four recently developed metaheuristic techniques. The computational results are encouraging and demonstrate that the DS-based clustering provides better values in terms of precision, recall and G-Measure.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journal of Social Sciences & Humanities
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-24-2-7
dc.subjectData clustering
dc.subjectDifferential search algorithm
dc.subjectMetaheuristic
dc.titleData clustering using differential search algorithm
dc.typeJournal Article
dc.format.volume24
dc.format.pages295-306
dc.format.issue2
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