Interpretable Sequence Clustering
This addresses interpretability challenges in sequence clustering for fields relying on categorical sequence analysis, though it appears incremental as it builds on existing clustering and pattern mining techniques.
The paper tackles the problem of uninterpretable categorical sequence clustering by proposing Interpretable Sequence Clustering Tree (ISCT), which combines sequential patterns with a tree structure to provide intuitive cluster explanations while achieving fast and accurate results on 14 real-world datasets.
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.