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An Improved Bisecting K-means Algorithm for Text Document Clustering

International Journal of Knowledge Based Computer Systems

Volume 4 Issue 2

Published: 2016
Author(s) Name: Janani Balakumar | Author(s) Affiliation: Bharathiar University, Coimbatore, Tamil Nadu, India
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Abstract

Cluster analysis is an unsupervised learning approach that aims to group the objects into different groups or clusters. So that each cluster can contain similar objects with respect to any predefined condition. Text document clustering is the important technique of text mining in efficiently organizing the large volume of documents into a small number of significant clusters. The main objective of this research work is to cluster the collection of documents into related groups based on the contents of the particular documents. In order to perform this clustering task, this research work makes use of two existing algorithms, namely K-means and Bisecting K-means algorithm, and also this research work proposes a new clustering algorithm namely Enhanced-Bisecting K-means algorithm. From the experimental results it is observed that the proposed algorithm gives the better clustering accuracy than other algorithms.

Keywords: Text Mining, Text Document Clustering,K-means, Bisecting K-means, Enhanced Bisecting K-means

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