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, University of Oviedo,
Oviedo, Spain. E-mail: luciano@lsi.uniovi.es
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.
