A Subsequence Interleaving Model for Sequential Pattern Mining

TL;DR

This paper introduces a probabilistic subsequence interleaving model for sequential pattern mining that efficiently identifies relevant, interpretable, and non-redundant patterns without relying on predefined encoding schemes.

Contribution

The paper proposes a novel probabilistic model and inference framework for sequential pattern mining that improves pattern relevance and interpretability over existing methods.

Findings

Effective in both synthetic and real datasets

Produces patterns with low redundancy and high interpretability

Comparable or superior to state-of-the-art algorithms

Abstract

Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database. We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, which allows us to search for the most compressing set of patterns without designing a specific encoding scheme. Our proposed algorithm is able to efficiently mine the most relevant sequential patterns and rank them using an associated measure of interestingness. The efficient inference in our model is a direct result of our use of a structural expectation-maximization framework, in which the expectation-step takes the form of a submodular optimization problem subject to a coverage constraint. We show on both synthetic and real world datasets that our model mines a set of sequential patterns with low spuriousness and redundancy, high interpretability and usefulness in real-world applications. Furthermore, we demonstrate that the quality of the patterns from our approach is comparable to, if not better than, existing state of the art sequential pattern mining algorithms.