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Clustering Ensembles for Categorical Data

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. In this paper we focus on the design of ensembles for categorical data. Our approach leverages diverse input clusterings discovered in random subspaces. We experimentally demostrate the efficacy of our technique in combination with the categorical clustering algorithm COOLCAT.