Free-rider Episode Screening via Dual Partition Model

TL;DR

This paper introduces EDP, a dual partition model that effectively filters free-rider episodes in frequent pattern mining by distinguishing real patterns from noise using a novel support expectation framework.

Contribution

The paper proposes a new dual partition strategy and a novel expected support definition for free-rider episode filtering, improving over existing methods.

Findings

EDP outperforms state-of-the-art methods in synthetic datasets.

EDP effectively filters free-rider episodes in real-world datasets.

The dual partition approach enhances the detection of true patterns.

Abstract

One of the drawbacks of frequent episode mining is that overwhelmingly many of the discovered patterns are redundant. Free-rider episode, as a typical example, consists of a real pattern doped with some additional noise events. Because of the possible high support of the inside noise events, such free-rider episodes may have abnormally high support that they cannot be filtered by frequency based framework. An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently. In this paper, we take more complex subepisodes into consideration and develop a novel partition model named EDP for free-rider episode filtering from a given set of episodes. It combines (1) a dual partition strategy which divides an episode to an underlying real pattern and potential noises; (2) a novel definition of the expected support of a free-rider episode based on the proposed partition strategy. We can deem the episode interesting if the observed support is substantially higher than the expected support estimated by our model. The experiments on synthetic and real-world datasets demonstrate EDP can effectively filter free-rider episodes compared with existing state-of-the-arts.