Message Passing Clustering (MPC): a knowledge-based framework for clustering under biological constraints

被引:9
作者
Geng, Huimin [1 ]
Deng, Xutao [1 ]
Ali, Hesham H. [1 ]
机构
[1] Univ Nebraska, Dept Comp Sci, Omaha, NE 68182 USA
关键词
clustering; phylogenetics; microarray gene expression; feature scaling; stochastic process; semisupervised; Message Passing Clustering; MPC; clustering algorithms; data mining; bioinformatics;
D O I
10.1504/IJDMB.2008.019092
中图分类号
Q [生物科学];
学科分类号
07 [理学]; 0710 [生物学]; 09 [农学];
摘要
A new clustering algorithm, Message Passing Clustering (MPC), is proposed. MPC employs the concept of message passing to describe parallel and spontaneous,clustering process by allowing data oblicate objectss to communicate with each other. MPC also provides in extensible framework to accommodate additional features into clustering, Rich as adaptive Feature weights scaling, stochastic cluster merging, and semi-supervised constraints guiding. Extensive experiments were performed Using both simulation and real microarray gene expression and phylogenetic data. The results showed that MPC performed favourably to other popular clustering algorithms and MPC with the integration of additional features gave even higher accuracy rate than MPC.
引用
收藏
页码:95 / 120
页数:26
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