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Algorithms 2018, 11(10), 151;

K-Means Cloning: Adaptive Spherical K-Means Clustering

Department of Computer Science, Faculty of Comp. & Info, Assiut University, Assiut 71526, Egypt
Department of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi Arabia
Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut Branch, Assiut 71524, Egypt
Electrical Engineering Department, Assiut University, Assiut 71516, Egypt
Author to whom correspondence should be addressed.
Received: 17 May 2018 / Revised: 22 September 2018 / Accepted: 25 September 2018 / Published: 6 October 2018
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We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster ‘colonies’ to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm. View Full-Text
Keywords: data mining; clustering analysis; adaptive K-means; simulated annealing data mining; clustering analysis; adaptive K-means; simulated annealing

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Hedar, A.-R.; Ibrahim, A.-M.M.; Abdel-Hakim, A.E.; Sewisy, A.A. K-Means Cloning: Adaptive Spherical K-Means Clustering. Algorithms 2018, 11, 151.

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