This paper proposes a general framework for classifying data streams by exploiting incremental clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual feature space is proposed. The clustering algorithm is then applied in order to group different concepts and identify recurring contexts. The ensemble is produced by maintaining an classifier for every concept discovered in the stream

The full version of this paper as well as the datasets used for evaluation can be found at: http://mlkd.csd.auth.gr/concept_drift.html