APLICACION DE LA BUSQUEDA TABU EN LA CLASIFICACION POR PARTICIONES

Abstract

We present an improved method for clustering by using the combinatorial optimization technique called tabu search, for obtaining homogeneous and well-separated classes. The algorithm intends to find the optimal partition of a set of objects from the point of view of the within-classes variance criterion, trying to escape from local minima. Two versions of the method are presented: the original one, that introduces the variance value in tabu list, and the improved one, that penalizes only some partition features. Differences and comparisons are pointed out