基于概率模型的重叠子空间聚类算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201707004

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Abstract Due to the low clustering accuracy of the existing subspace clustering methods in dealing with the problem of overlapping clusters, an overlapping subspace clustering algorithm based on probability model(OSCPM) is proposed. Firstly, the high-dimensional data is divided into several subspaces by using the subspace representation of mixed-norm. Then, a probability model of the exponential family distribution is used to determine the overlapping part of the clusters in the subspace, and the data is assigned to the correct class clusters to get the clustering results. An alternating maximization method is used to determine the optimal solution of the objective function in the process of parameter estimation. Experimental results on artificial datasets and UCI datasets show that OSCPM produces better clustering performance compared with other algorithms and it is suitable for large scale datasets.

Key words： Overlapping Subspace Clustering Mixed-Norm Subspace Representation Probabilistic Model Alternating Maximization

Received: 18 January 2017

ZTFLH:

TP 181

Fund:Supported by Young Scientists Fund of National Natural Science Foundation of China(No.61401185)

About author:: (QIU Yunfei, born in 1976, Ph.D., professor. His research interests include data mining and intelligent data processing.)
(FEI Bowen, born in 1991, Ph.D. candidate. Her research interests include data mi-ning and intelligent data processing.)
(LIU Daqian, born in 1992, Ph.D. candidate. His research interests include image and vision computing, object detection and tra-cking.)

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QIU Yunfei,FEI Bowen,LIU Daqian. Overlapping Subspace Clustering Based on Probabilistic Model[J]. , 2017, 30(7): 609-621.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201707004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I7/609

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