A single pass trellis-based algorithm for clustering evolving data streams

Abstract

The main paradigm for clustering evolving data streams in the last 10 years has been to divide the clustering process into an online phase that computes and stores detailed statistics about the data in micro-clusters and an offline phase that queries micro-cluster statistics and returns desired clustering structures. The argument for two-phase algorithms is that they support evolving data streams and temporal multi-scale analysis, which single pass algorithms do not. In this paper, we describe a single pass fully online trellis-based algorithm, named ClusTrel, designed for centroid-based clustering that supports evolving data streams and generates clustering structures right after a new point is processed. The performance of ClusTrel is assessed and compared to state of the art algorithms for clustering of data streams showing similar performance with smaller memory footprint. © 2012 Springer-Verlag.