A method for clustering transient data streams

摘要

This paper describes a novel method for clustering single and multi-dimensional data streams. With incremental computation of the incoming data, our method determines if the cluster formation should change from an initial cluster formation. Four main types of cluster evolutions are studied: cluster appearance, cluster disappearance, cluster splitting, and cluster merging. We present experimental results of our algorithms both in terms of scalability and cluster quality, compared with recent work in this area.