Constraint-Based Subspace Clustering.

In high dimensional data, the general performance of traditional clustering algorithms decreases. This is partly because the similarity criterion used by these algorithms becomes inadequate in high dimensional space. Another reason is that some dimensions are likely to be irrelevant or contain noisy data, thus hiding a possible clustering. To overcome these problems, subspace clustering techniques, which can automatically find clusters in relevant subsets of dimensions, have been developed. However, due to the huge number of subspaces to consider, these techniques often lack efficiency. In this paper we propose to extend the framework of bottomup subspace clustering algorithms by integrating background knowledge and, in particular, instance-level constraints to speed up the enumeration of subspaces. We show how this new framework can be applied to both density and distancebased bottom-up subspace clustering techniques. Our experiments on real datasets show that instance-level constr...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Élisa Fromont, Adriana Prado, Céline Robardet
• Keywords: Computer Science, SDM 2009, Subspace Clustering Algorithms, Subspace Clustering Techniques, Traditional Clustering Algorithms