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Initial centroid selection for K- means clustering algorithm using the statistical method
1 Department of Computer Science, Sri Meenakshi Government Arts College for Women, Madurai, Tamil Nadu, India.
2 Standard Chartered Global Business Services Sdn Bhd, Kuala Lumpur, Malaysia.
3 Department of Physics, Government Arts College, Melur, Tamil Nadu, India.
Research Article
International Journal of Science and Research Archive, 2022, 07(02), 474-478
Article DOI: 10.30574/ijsra.2022.7.2.0309
Publication history:
Received on 07 November 2022; revised on 20 December 2022; accepted on 22 December 2022
Abstract:
An iterative process that converges to one of the many local minima is used in practical clustering methods. K-means clustering is one of the most well-liked clustering methods. It is well known that these iterative methods are very susceptible to the initial beginning circumstances. In order to improve K-means clustering's performance, this research suggests a novel method for choosing initial centroids. The suggested approach is evaluated with online access records, and the results demonstrate that better initial starting points and post-processing cluster refinement result in better solutions.
Keywords:
Web data clustering; Web Usage Mining; K-means; Initial Centroids; Web access logs; Genetic Algorithm
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
