Towards subspace clustering on dynamic data: an incremental version of PreDeCon

Abstract

Todays data are high dimensional and dynamic, thus clustering over such kind of data is rather complicated. To deal with the high dimensionality problem, the subspace clustering research area has lately emerged that aims at finding clusters in subspaces of the original feature space. So far, the subspace clustering methods are mainly static and thus, cannot address the dynamic nature of modern data. In this paper, we propose an incremental version of the density based projected clustering algorithm PreDeCon, called incPreDeCon. The proposed algorithm efficiently updates only those subspace clusters that might be affected due to the population update.