基于隐含变量的聚类集成模型

聚类集成能成为机器学习活跃的研究热点,是因为聚类集成能够保护私有信息、分布式处理数据和对知识进行重用,此外,噪声和孤立点对结果的影响较小.主要工作包括:第一,分析了把每一个基聚类器看成是原数据的一个属性这种处理方式的优越性,发现按此方法建立起来的聚类集成算法就具有良好的扩展性和灵活性;第二,在此基础之上,建立了latent variable cluster ensemble(LVCE)概率模型进行聚类集成,并且给出了LVCE 模型的Markovchain Monte Carlo(MCMC)算法.实验结果表明,LVCE 模型的MCMC 算法能够进行聚类集成并且达到良好的效果,同时可以体现数据聚类的紧密程度.

A Latent Variable Model for Cluster Ensemble

Cluster ensemble becomes a research focus due to its success in privacy protection, distributing computing and reusing knowledge. Furthermore, the noise and isolation have little effect on the final result. Thereare two contributions in this paper. First, by regarding every base clustering as one attribute of the original data, it has found that the algorithm based on that is more extendable and flexible. Second, it designs a latent variable cluster ensemble (LVCE) model in this way and infers the algorithm of the model with Markov chain Monte Carlo (MCMC) approximation. At the end of the paper, the experimental results show that the MCMC algorithm of LVCE has a better result and can show the compactedness of data points clustering.