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Clustering Using Multi-objective Differential Evolution Algorithms

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Metaheuristic Clustering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 178))

Abstract

This chapter introduces the task of fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of four recently developed multi-objective variants of Differential Evolution (DE) algorithm over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of four DE variants have also been contrasted to that of two most well-known schemes of MO clustering namely the Non Dominated Sorting Genetic Algorithm ( NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results over four artificial and four real life datasets of varying range of complexities indicates that DE holds immense promise as a candidate algorithm for devising MO clustering schemes .

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Authors
  1. Swagatam Das
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  2. Ajith Abraham
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  3. Amit Konar
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© 2009 Springer-Verlag Berlin Heidelberg

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Das, S., Abraham, A., Konar, A. (2009). Clustering Using Multi-objective Differential Evolution Algorithms. In: Metaheuristic Clustering. Studies in Computational Intelligence, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93964-1_6

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  • DOI: https://doi.org/10.1007/978-3-540-93964-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92172-1

  • Online ISBN: 978-3-540-93964-1

  • eBook Packages: EngineeringEngineering (R0)

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