Drifted data stream clustering based on clustree algorithm

Abstract

Correct recognition of the possible changes in data streams, called concept drifts plays a crucial role in constructing the appropriate model learning strategy. This paper focuses on the unsupervised learning model for non-stationary data streams, where two significant modifications of the ClustTree algorithm are presented. They allow the clustering model to be adapted to the changes caused by a concept drift. An experimental study conducted on a set of benchmark data streams proves the usefulness of the proposed solutions.