SSC: Statistical Subspace Clustering

Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. This is a particularly important challenge with high dimensional data where the curse of dimensionality occurs. It has also the benefit of providing smaller descriptions of the clusters found. Existing methods only consider numerical databases and do not propose any method for clusters visualization. Besides, they require some input parameters difficult to set for the user. The aim of this paper is to propose a new subspace clustering algorithm, able to tackle databases that may contain continuous as well as discrete attributes, requiring as few user parameters as possible, and producing an interpretable output. We present a method based on the use of the well-known EM algorithm on a probabilistic model designed under some specific hypotheses, allowing us to present the result as a set of rules, each one defined with as few relevant dimensions as possi...

• Type: Conference
• Year: 2005
• Where: MLDM
• Authors: Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet
• Keywords: Machine Learning, MLDM 2005, Subspace Clustering, Subspace Clustering Algorithm, Traditional Clustering