Knowledge-based evolving clustering algorithm for data stream

Abstract

In this paper, we present a knowledge-based evolving algorithm for data stream clustering. The basic idea of the new algorithm is to divide data stream into frames, and to incorporate knowledge learned in previous frames into clustering of the following ones. Experimental studies have demonstrated that the evolving learning mechanism leads to improved clustering results compared with conventional incremental clustering algorithm Fuzzy ART and batch-based clustering algorithm k-means.