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A Comparative Performance Analysis of Clustering Algorithms

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Executive Summary

Clustering is an adaptive procedure in which objects are clustered or grouped together, based on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Various clustering algorithms have been developed which results to a good performance on datasets for cluster formation. This paper analyze the three major clustering algorithms: K-Means, Farthest First and Hierarchical clustering algorithm and compare the performance of these three major clustering algorithms on the aspect of correctly class wise cluster building ability of algorithm. The results are tested on three datasets namely Wine, Haberman and Iris dataset using WEKA interface and compute the correctly cluster building instances in proportion with incorrectly formed cluster.

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