An arbitrary shape clustering algorithm over variable density data streams

Abstract

This paper proposes VDStream, a new effective method, to discover arbitrary shape clusters over variable density data streams. The algorithm can reduce the influence of history data and effectively eliminate the interference of noise data. When the density of data streams changes, VDStream can dynamically adjust the parameters of density to find precise clusters. Experiments demonstrate the effectiveness and efficiency of VDStream.