用谱图划分方法实现通用可解释聚类，适配任意聚类结果。
SpEx: A Spectral Approach to Explainable Clustering
- 基于谱图划分构建通用可解释聚类框架，无需限定聚类目标。
- 在多个数据集上性能优于基线方法，支持直接建模或适配现有聚类。
- 首次将已有算法统一为双图割优化视角，揭示其内在机制。
Moshkovitz 等人（2020）提出的轴对齐决策树可解释聚类已引起广泛关注。以往研究主要针对特定聚类目标最小化可解释性代价，缺乏一种通用方法将解释树拟合到任意给定的非可解释聚类结果上。本文提出一种新的、通用的可解释聚类方法，基于谱图划分。该方法可将解释树拟合到任意非可解释聚类结果，或直接作用于原始数据集。此外，我们证明先前算法也可通过 Trevisan（2013）提出的广义框架进行解释——在两个图上同时优化割集。实验表明，本文方法在多个数据集上均优于基线模型。
原文摘要 · Abstract (English)
Explainable clustering by axis-aligned decision trees was introduced by Moshkovitz et al. (2020) and has gained considerable interest. Prior work has focused on minimizing the price of explainability for specific clustering objectives, lacking a general method to fit an explanation tree to any given clustering, without restrictions. In this work, we propose a new and generic approach to explainable clustering, based on spectral graph partitioning. With it, we design an explainable clustering algorithm that can fit an explanation tree to any given non-explainable clustering, or directly to the dataset itself. Moreover, we show that prior algorithms can also be interpreted as graph partitioning, through a generalized framework due to Trevisan (2013) wherein cuts are optimized in two graphs simultaneously. Our experiments show the favorable performance of our method compared to baselines on a range of datasets.
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