Random Subspace Ensembles for Clustering Categorical Data

Abstract

Cluster ensembles provide a solution to challenges inherent to clustering arising from its ill-posed nature. In fact, cluster ensembles can find robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this chapter we focus on the design of ensembles for categorical data. Our techniques build upon diverse input clusterings discovered in random subspaces, and reduce the problem of defining a consensus function to a graph partitioning problem. We experimentally demonstrate the efficacy of our approach in combination with the categorical clustering algorithm COOLCAT.