In this paper we present a Pareto based multi objective algorithm for semi supervised clustering (PSC). Semi-supervised clustering uses a small amount of supervised data known as constraints, to assist unsupervised learning. Instead of modifying the clustering objective function, we add another objective function to satisfy specified constraints. We use a lexicographically ordered cluster assignment step to direct the search and a Pareto based multi objective evolutionary algorithm to maintain diversity in the population. Two objectives are considered: one that minimizes the intra cluster variance and another that minimizes the number of constraint violations. Experiments show the superiority of the method over a greedy algorithm (PCK-means) and a genetic algorithm (COP-HGA). © 2012 Springer-Verlag.
CITATION STYLE
Denoyer, L., & Gallinari, P. (2003). Machine Learning and Data Mining in Pattern Recognition. (P. Perner & A. Rosenfeld, Eds.), MLDM (Vol. 2734, pp. 328–342). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-45065-3
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