Abstract
Clustering is the popular unsupervised learning technique of data mining which divide the data into groups having similar objects and used in various application areas. k-Means is the most popular clustering algorithm among all partition based clustering algorithm to partition a dataset into meaningful patterns. k-Means suffers some shortcomings. This paper addresses two shortcomings of k-Means; pass number of centroids in apriori and does not handle noise. This paper also presents an overview of cluster analysis, clustering algorithms, preprocessing and normalization techniques in modified k-Means to improve the effectiveness and efficiency of the modified k-Means clustering algorithm.
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Patel, V.R., Mehta, R.G. (2011). Modified k-Means Clustering Algorithm. In: Das, V.V., Thankachan, N. (eds) Computational Intelligence and Information Technology. CIIT 2011. Communications in Computer and Information Science, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25734-6_46
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DOI: https://doi.org/10.1007/978-3-642-25734-6_46
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