Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis
- Authors
- Suresh, Kaushik; Kundu, Debarati; Ghosh, Sayan; Das, Swagatam; Abraham, Ajith; Han, Sang Yong
- Issue Date
- May-2009
- Publisher
- MDPI
- Keywords
- differential evolution; multi-objective optimization; fuzzy clustering; micro-array data clustering
- Citation
- SENSORS, v.9, no.5, pp 3981 - 4004
- Pages
- 24
- Journal Title
- SENSORS
- Volume
- 9
- Number
- 5
- Start Page
- 3981
- End Page
- 4004
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65254
- DOI
- 10.3390/s90503981
- ISSN
- 1424-8220
1424-3210
- Abstract
- 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.
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