用粗糙集提升图谱聚类结果的可解释性
Rough Sets for Explainability of Spectral Graph Clustering
- 引入粗糙集理论增强聚类解释能力
- 解决无意义文档与算法随机性带来的解释难题
- 适合需要可解释聚类结果的研究者
图谱聚类方法（GSC）能有效识别形状、密度各异的簇，但其结果在应用于文本文档时难以向用户解释，主要因嵌入谱空间与文本内容无直接关联。此外，无明确语义的文档及聚类算法的随机性进一步削弱了可解释性。本文提出一种改进的解释方法，借鉴粗糙集理论，有效缓解上述问题，提升聚类结果的可理解性。
原文摘要 · Abstract (English)
Graph Spectral Clustering methods (GSC) allow representing clusters of diverse shapes, densities, etc. However, the results of such algorithms, when applied e.g. to text documents, are hard to explain to the user, especially due to embedding in the spectral space which has no obvious relation to document contents. Furthermore, the presence of documents without clear content meaning and the stochastic nature of the clustering algorithms deteriorate explainability. This paper proposes an enhancement to the explanation methodology, proposed in an earlier research of our team. It allows us to overcome the latter problems by taking inspiration from rough set theory.
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