Author(s): Mrs. S. Sujatha, Mrs. A. Shanthi Sona
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol.2 - Issue 2 (February - 2013)
Abstract---Cluster analysis is a major technique for classifying a ?mountain? of information into manageable meaningful piles. It is a data reduction tool that creates subgroups that are more manageable than individual datum. The fundamental data clustering problem may be defined as discovering groups in data or grouping similar objects together. The goal of clustering is to find groups of similar objects based on a similarity metric. However, a similarity metric is mainly defined by the user to ensure it suits his needs. Until now, there is still no absolute measure that always fit all applications. Some of the problems associated with current clustering algorithms are that they do not address all the requirements adequately, and need high time complexity when dealing with a large number of dimensions and large data sets. K-Means is one of the algorithms that solve the well known clustering problem. The algorithm classifies objects to a pre- defined number of clusters, which is given by the user (assume k clusters). The idea is to choose random cluster centers, one for each cluster. These centers are preferred to be as far as possible from each other. Starting points affect the clustering process and results. Here the Centroid initialization plays an important role in determining the cluster assignment in effective way. Also, the convergence behavior of clustering is based on the initial centroid values assigned. This paper focuses on the assignment of cluster centroid selection so as to improve the clustering performance by K-Means clustering algorithm. This paper uses Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance to assign for cluster centroid. Experimental result suggests that the proposed approach results in better clustering result when compared to the conventional technique.
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