  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.
