Método heurístico para particionamiento óptimo - Sergio Gerardo de los Cobos Silva; Javier Trejos Zelaya; Blanca Rosa Pérez Salvador; Miguel Ángel Gutiérrez Andrade (Revista de Matemática: Teoría y Aplicaciones, 2009)
Many data analysis problems deal with non supervised partitioning of a data set, in non empty clusters well separated between them and homogeneous within the clusters. An ideal partitioning is obtained when any object can be assigned a class without ambiguity. The present paper has two main parts; first, we present different methods and heuristics that find the number of clusters for optimal partitioning of a set; afterwards, we propose a new heuristic and we perform different comparisons in order to evaluate the advantages on well known data sets; we end the paper with some concluding remarks. Keywords: Optimal partitioning, clustering, classification, heuristics.