Alternative clustering by utilizing multi-objective genetic algorithm with linked-list based chromosome encoding

Authors:
Jun Du;Emin Erkan Korkmaz;Reda Alhajj;Ken Barker
Affiliations:
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada;Department of Computer Engineering, Yeditepe University, Kadikoy, Istanbul, Turkey;Department of Computer Science, University of Calgary, Calgary, Alberta, Canada;Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
Venue:
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2005

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Abstract

In this paper, we present a linked-list based encoding scheme for multiple objectives based genetic algorithm (GA) to identify clusters in a partition. Our approach obtains the optimal partitions for all the possible numbers of clusters in the Pareto Optimal set returned by a single genetic GA run. The performance of the proposed approach has been tested using two well-known data sets, namely Iris and Ruspini. The obtained results are promising and demonstrate the applicability and effectiveness of the proposed approach.