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A New Evolutionary Algorithm for Determining the Optimal Number of Clusters

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2 Author(s)
Wei Lu ; University of Victoria, Canada ; I. Traore

Estimating the optimal number of clusters for a dataset is one of the most essential issues in cluster analysis. An improper pre-selection for the number of clusters might easily lead to bad clustering outcome. In this paper, we propose a new evolutionary algorithm to address this issue. Specifically, the proposed evolutionary algorithm defines a new entropy-based fitness function, and three new genetic operators for splitting, merging, and removing clusters. Empirical evaluations using the synthetic dataset and an existing benchmark show that the proposed evolutionary algorithm can exactly estimate the optimal number of clusters for a set of data

Published in:

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06)  (Volume:1 )

Date of Conference:

28-30 Nov. 2005