| Autor: |
Sang Yong Han, Swagatam Das, Ajith Abraham, Kaushik Suresh, Debarati Kundu, Sayan Ghosh |
| Jazyk: |
angličtina |
| Rok vydání: |
2009 |
| Předmět: |
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| Zdroj: |
Sensors, Vol 9, Iss 5, Pp 3981-4004 (2009) |
| Druh dokumentu: |
article |
| ISSN: |
1424-8220 |
| DOI: |
10.3390/s90503981 |
| Popis: |
This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE 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 the multi-objective 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 using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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