---
title: "A Probabilistic Approach to Latent Cluster Analysis"
authors:
  - "Zhipeng Xie"
  - "Rui Dong"
  - "Zhengheng Deng"
  - "Zhenying He"
  - "Weidong Yang"
doi: ""
url: "https://ijcai.org/proceedings/2013/268"
pdf: "https://ijcai.org/proceedings/2013/0268.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1813-1818"
track: ""
keywords:
---

# A Probabilistic Approach to Latent Cluster Analysis

Zhipeng Xie, Rui Dong, Zhengheng Deng, Zhenying He, Weidong Yang

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 1813-1818.

## Abstract

Facing a large number of clustering solutions, cluster ensemble method provides an effective approach to aggregating them into a better one. In this paper, we propose a novel cluster ensemble method from probabilistic perspective. It assumes that each clustering solution is generated from a latent cluster model, under the control of two probabilistic parameters. Thus, the cluster ensemble problem is reformulated into an optimization problem of maximum likelihood. An EM-style algorithm is designed to solve this problem. It can determine the number of clusters automatically. Experimental results have shown that the proposed algorithm outperforms the state-of-the-art methods including EAC-AL, CSPA, HGPA, and MCLA. Furthermore, it has been shown that our algorithm is stable in the predicted numbers of clusters.