Scalable Contrast Pattern Mining over Data Streams

Abstract

Incremental contrast pattern mining (CPM) is an important task in various fields such as network traffic analysis, medical diagnosis, and customer behavior analysis. Due to increases in the speed and dimension of data streams, a major challenge for CPM is to deal with the huge number of generated candidate patterns. While there are some works on incremental CPM, their approaches are not scalable in dense and high dimensional data streams, and the problem of CPM over an evolving dataset is an open challenge. In this work we focus on extracting the most specific set of contrast patterns (CPs) to discover significant changes between two data streams. We devise a novel algorithm to extract CPs using previously mined patterns instead of generating all patterns in each window from scratch. Our experimental results on a wide variety of datasets demonstrate the advantages of our approach over the state of the art in terms of efficiency.