EVALUACIÓN DE HEURÍSTICAS DE OPTIMIZACIÓN COMBINATORIA EN CLASIFICACIÓN POR PARTICIONES

Abstract

The aim of this paper is to present the results of the evaluation of combinatorial optimization heuristic applied to obtain partitions in clustering: simulated annealing, tabu search and a genetic algorithm, using data tables generated randomly according to some defined parameters. Those techniques were compared between them and with traditional methods (k-means and Ward’s agglomerative clustering). Sixteen tables were generated with normally distributed variables and for each one, the experiment was
repeated 100 times for each method. The intra-classes inertia was used as criterion to compare the classifications obtained. Best results were obtained for simulated annealing and the genetic algorithm.