Mining data streams under block evolution

Abstract

In this paper we survey recent work on incremental data mining model maintenance and change detection under block evolution. In block evolution, a dataset is updated periodically through insertions and deletions of blocks of records at a time. We describe two techniques: (1) We describe a generic algorithm for model maintenance that takes any traditional incremental data mining model maintenance algorithm and transforms it into an algorithm that allows restrictions on a temporal subset of the database. (2) We also describe a generic framework for change detection, that quantifies the difference between two datasets in terms of the data mining models they induce.