Mining Representative Subspace Clusters in High-dimensional Data

Abstract

A major challenge in subspace clustering is that subspace clustering may generate an explosive number of clusters with high computational complexity, which severely restricts the usage of subspace clustering. The problem gets even worse with the increase of the data’s dimensionality. In this paper, we propose to mine the representative subspace clusters in high-dimensional data to alleviate the problem. Typically, subspace clusters can be clustered further into groups, and several representative clusters can be generated from each group. Unfortunately, when the size of the set of representative clusters is specified, the problem of finding the optimal set is NP-hard. To solve this problem efficiently, we present an approximate method PCoC. The greatest advantage of our method is that we only need a subset of subspace clusters as the input. Our performance study shows the effectiveness and efficiency of the method.