A GRASP Algorithm for Clustering

Authors:
J. R. Cano;Oscar Cordón;Francisco Herrera;Luciano Sánchez
Affiliations:
-;-;-;-
Venue:
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Year:
2002

Citing 1
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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 for getting initial solutions and K-Means algorithm as a local search algorithm. We compare it with some typical initialization methods: Random, Forgy, Macqueen and Kaufman. The new approach obtains high quality solutions for the benchmark problems.