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Contrast pattern mining (CPM) aims to discover patterns whose supportincreases significantly from a background dataset compared to a target dataset.CPM is particularly useful for characterising changes in evolving systems,e.g., in network traffic analysis to detect unusual activity. While mostexisting techniques focus on extracting either the whole set of contrastpatterns (CPs) or minimal sets, the problem of efficiently finding a relevantsubset of CPs, especially in high dimensional datasets, is an open challenge.In this paper, we focus on extracting the most specific set of CPs to discoversignificant changes between two datasets. Our approach to this problem usesclosed patterns to substantially reduce redundant patterns. Our experimentalresults on several real and emulated network traffic datasets demonstrate thatour proposed unsupervised algorithm is up to 100 times faster than an existingapproach for CPM on network traffic data [2]. In addition, as an application ofCPs, we demonstrate that CPM is a highly effective method for detection ofmeaningful changes in network traffic.

Improving Scalability of Contrast Pattern Mining for Network Traffic Using Closed Patterns