Clustering With Multiview Explanations

Explainable clustering has become increasingly important as it presents the clustering results in a manner that can be easily understood by the end-users. However, most existing explainable clustering approaches generate only a single explanation such as a decision tree for the given clustering result, overlooking that multiple valid interpretations may exist. To address this limitation, we propose the CME (Clustering with Multiview Explanations) algorithm. This method constructs a diverse set of candidate decision trees by retaining multiple splitting points at each node, where each splitting point is valid in a statistical sense. The tree similarity based on optimal path matches across corresponding clusters is then used to build a similarity graph. A subset of representative decision trees are selected by solving the minimum dominating set problem defined on the corresponding similarity graph. Experiments on 10 real-world categorical datasets demonstrate that CME provides multiple complementary explanations that may be missed by existing algorithms. Moreover, these additional decision trees can be more accurate and interpretable than those ones identified by baseline methods.