A density-based clustering over evolving heterogeneous data stream

Abstract

Data stream clustering is an importance issue in data stream mining. In most of the existing algorithms, only the continuous features are used for clustering. In this paper, we introduce an algorithm HDenStream for clustering data stream with heterogeneous features. The HDenstream is also a density-based algorithm, so it is capable enough to cluster arbitrary shapes and handle outliers. Theoretic analysis and experimental results show that HDenStream is effective and efficient.