A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure

Authors:
J. R. Cano;O. Cordón;F. Herrera;L. Sánchez
Affiliations:
Department of Software Engineering, University of Huelva, 21071 La Rabida (Huelva), Spain. E-mail: Jose.cano@diesia.uhu.es;Department of Computer Science and A.I., University of Granada, 18071-Granada, Spain. E-mail: {ocordon,herrera}@decsai.ugr.es;Department of Computer Science and A.I., University of Granada, 18071-Granada, Spain. E-mail: {ocordon,herrera}@decsai.ugr.es;Department of Computer Science, University of Oviedo, Oviedo, Spain. E-mail: luciano@lsi.uniovi.es
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - IBERAMIA '02
Year:
2002

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Abstract

We present a new approach for Cluster Analysis based on a Greedy Randomized Adaptive Search Procedure (GRASP), with the objective of overcoming the convergence to a local solution. It uses a probabilistic greedy Kaufman initialization to get initial solutions and K-Means as a local search algorithm. The approach is a new initialization one for K-Means. Hence, we compare it with some typical initialization methods: Random, Forgy, Macqueen and Kaufman. Our empirical results suggest that the hybrid GRASP - K-Means with probabilistic greedy Kaufman initialization performs better than the other methods with improved results. The new approach obtains high quality solutions for eight benchmark problems.