Entropy Based Cluster Selection

Clustering has emerged as a method of unsupervised partitioning of a given set of data instances into a number of groups (called clusters) so that instances in the same group are more similar among each other with respect to instances in other groups. But there does not exist a universal clustering algorithm that can yield satisfactory result for any dataset. In this work we consider an ensemble (collection) of clusterings (partitions) of a dataset obtained in different ways and devise two methods that judiciously select clusters from different clusterings in the ensemble to construct a robust clustering. The superior performances of the proposed methods over well-known existing clustering algorithms on several benchmark datasets are empirically reported.