Interpretable Clustering with Multi-View Generative Model

Clustering has two problems: multi-view and interpretation. In this paper, we propose an interpretable clustering with multi-view generative model (ICMG). ICMG can get multiple clustering based multi-view meanwhile qualitatively and quantitatively interpret clustering results by using semantic information in views. Firstly, we construct a multi-view generative model (MGM). It generates multiple views by using Bayesian program learning (BPL) and multi-view Bayesian case model (MBCM). Then we get multiple clustering by clustering based on views’ matching degree. Finally, ICMG qualitatively and quantitatively interprets clustering results by using semantic information in views’ prototypes and important features. Experimental results show ICMG can get multiple interpretable clustering and the performance of ICMG is superior to traditional multi-view clustering.