# Interpretable Sequence Clustering

arXiv: https://arxiv.org/abs/2309.01140 · HTML: https://mlsanity.com/paper/2309.01140/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 3 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [Nearly-Tight and Oblivious Algorithms for Explainable Clustering](https://mlsanity.com/paper/2106.16147/) (2021)
- [How to Find a Good Explanation for Clustering?](https://mlsanity.com/paper/2112.06580/) (2021)
- [Explainable k-means. Don't be greedy, plant bigger trees!](https://mlsanity.com/paper/2111.03193/) (2021)
- [Interpretable Clustering via Multi-Polytope Machines](https://mlsanity.com/paper/2112.05653/) (2021)
- [Sequence Graph Transform (SGT): A Feature Embedding Function for Sequence Data Mining](https://mlsanity.com/paper/1608.03533/) (2016)

## Built on by (most cited first, 1 of 1)

- [Interpretable Clustering: A Survey](https://mlsanity.com/paper/2409.00743/) (2024)

## Abstract (arXiv)

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.