Interpretable Sequence Clustering

TL;DR

This paper introduces Interpretable Sequence Clustering Tree (ISCT), a method that combines pattern mining with decision trees to produce understandable clusters from categorical sequences, achieving fast, accurate, and interpretable results.

Contribution

The paper presents ISCT, a novel approach that integrates sequential pattern mining with a decision tree framework for interpretable sequence clustering.

Findings

ISCT produces interpretable tree structures for sequence clusters.

The method achieves high accuracy and speed on real-world datasets.

Experimental results validate the interpretability and effectiveness of ISCT.

Abstract

Categorical sequence clustering plays a crucial role in various fields, but the lack of interpretability in cluster assignments poses significant challenges. Sequences inherently lack explicit features, and existing sequence clustering algorithms heavily rely on complex representations, making it difficult to explain their results. To address this issue, we propose a method called Interpretable Sequence Clustering Tree (ISCT), which combines sequential patterns with a concise and interpretable tree structure. ISCT leverages k-1 patterns to generate k leaf nodes, corresponding to k clusters, which provides an intuitive explanation on how each cluster is formed. More precisely, ISCT first projects sequences into random subspaces and then utilizes the k-means algorithm to obtain high-quality initial cluster assignments. Subsequently, it constructs a pattern-based decision tree using a boosting-based construction strategy in which sequences are re-projected and re-clustered at each node before mining the top-1 discriminative splitting pattern. Experimental results on 14 real-world data sets demonstrate that our proposed method provides an interpretable tree structure while delivering fast and accurate cluster assignments.