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

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