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Selection of Initial Centroids for k-Means Algorithm

Author(s): Anand M. Baswade | Prakash S. Nalwade

Journal: International Journal of Computer Science and Mobile Computing
ISSN 2320-088X

Volume: 2;
Issue: 7;
Start page: 161;
Date: 2013;
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Keywords: Data mining | clustering | k-Means

Clustering is one of the important data mining techniques. k-Means [1] is one of the mostimportant algorithm for Clustering. Traditional k-Means algorithm selects initial centroids randomly and ink-Means algorithm result of clustering highly depends on selection of initial centroids. k-Means algorithm issensitive to initial centroids so proper selection of initial centroids is necessary. This paper introduces anefficient method to start the k-Means with good initial centroids. Good initial centroids are useful for betterclustering.
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