ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (8): 1713-1723.doi: 10.7544/issn1000-1239.2017.20170175

Special Issue: 2017人工智能前沿进展专题

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Interpretable Clustering with Multi-View Generative Model

Pan Xiaoyan, Lou Zhengzheng, Ji Bo, Ye Yangdong   

  1. (School of Information Engineering Zhengzhou University, Zhengzhou 450001)
  • Online:2017-08-01

Abstract: 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.

Key words: Bayesian program learning (BPL), Bayesian case model (BCM), interpretable, multi-view, clustering

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