Rare pattern mining on data streams

Abstract

There has been some research in the area of rare pattern mining where the researchers try to capture patterns involving events that are unusual in a dataset. These patterns are considered more useful than frequent patterns in some domain, including detection of computer attacks, or fraudulent credit transactions. Until now, most of the research in this area concentrates only on finding rare rules in a static dataset. There is a proliferation of applications which generate data streams, such as network logs and banking transactions. Applying techniques for static datasets is not practical for data streams. In this paper we propose a novel approach called Streaming Rare Pattern Tree (SRP-Tree), which finds rare rules in a data stream environment using a sliding window, and show that it is faster than current approaches. © 2012 Springer-Verlag.