Soft Subspace Topological Clustering over Evolving Data Stream

Abstract

Subspace clustering has been successfully applied in many domains and its goal is to simultaneously detect both clusters and subspaces of the original feature space where these clusters exist. A Data stream is a massive sequences of data coming continuously. Clustering this type of data requires some restrictions in time and memory. In this paper we propose a new method named S2G-Stream based on clustering data streams and soft subspace clustering. Experiments on public datasets showed the ability of S2G-Stream to detect simultaneously the best features, subspaces and the best clustering.