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.
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© 2002 Springer-Verlag Berlin Heidelberg
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Cano, J., Cordón, O., Herrera, F., Sánchez, L. (2002). A GRASP Algorithm for Clustering. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_22
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DOI: https://doi.org/10.1007/3-540-36131-6_22
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