Experimental Estimation of Number of Clusters Based on Cluster Quality

Volume 12, Issue 4, pp 304-315 http://dx.doi.org/10.22436/jmcs.012.04.06

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Authors

G. Hannah Grace - Department of Mathematics, School of Advanced Sciences, VIT University, Chennai 600127, India. Kalyani Desikan - Department of Mathematics, School of Advanced Sciences, VIT University, Chennai 600127, India.

Abstract

Text Clustering is a text mining technique which divides the given set of text documents into significant clusters. It is used for organizing a huge number of text documents into a well-organized form. In the majority of the clustering algorithms, the number of clusters must be specified apriori, which is a drawback of these algorithms. The aim of this paper is to show experimentally how to determine the number of clusters based on cluster quality. Since partitional clustering algorithms are well-suited for clustering large document datasets, we have confined our analysis to a partitional clustering algorithm.

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Keywords

• clusters
• cluster quality
• CLUTO
• entropy
• purity.

MSC

•  62H30
•  68T10

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