Semi-supervised Constrained Clustering with Cluster Outlier Filtering

Constrained clustering addresses the problem of creating minimum variance clusters with the added complexity that there is a set of constraints that must be fulfilled by the elements in the cluster. Research in this area has focused on "must-link" and "cannot-link" constraints, in which pails of elements must be in the same or in different clusters, respectively. In this work we present a heuristic procedure to perform clustering in two classes when the restrictions affect all the elements of the two clusters in such a. way that they depend on the elements present; in the cluster. This problem is highly susceptible to outliers in each cluster (extreme values that create infeasible solutions), so the procedure eliminates elements with extreme values in both clusters, and achieves adequate performance measures at; the same time. The experiments performed on a company database allow to discover a great deal of information, with results that are more readily interpretable when compared to classical k-means clustering.