[2304.09989] CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance
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Computer Science > Machine Learning

arXiv:2304.09989 (cs)
[Submitted on 19 Apr 2023 (v1), last revised 1 May 2023 (this version, v2)]

Title:CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance

Authors:Abdesslem Layeb
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Abstract:This paper presents two novel deterministic initialization procedures for K-means clustering based on a modified crowding distance. The procedures, named CKmeans and FCKmeans, use more crowded points as initial centroids. Experimental studies on multiple datasets demonstrate that the proposed approach outperforms Kmeans and Kmeans++ in terms of clustering accuracy. The effectiveness of CKmeans and FCKmeans is attributed to their ability to select better initial centroids based on the modified crowding distance. Overall, the proposed approach provides a promising alternative for improving K-means clustering.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2304.09989 [cs.LG]
  (or arXiv:2304.09989v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2304.09989

Submission history

From: Abdesslem Layeb [view email]
[v1] Wed, 19 Apr 2023 21:46:02 UTC (611 KB)
[v2] Mon, 1 May 2023 17:13:38 UTC (607 KB)
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