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13th IEEE International Conference on BioInformatics and BioEngineering (2005)
Minneapolis, Minnesota
Oct. 19, 2005 to Oct. 21, 2005
ISBN: 0-7695-2476-1
pp: 294-298
Huimin Geng , University of Nebraska Medical Center
Xutao Deng , University of Nebraska at Omaha
Dhundy Bastola , University of Nebraska Medical Center
Hesham Ali , University of Nebraska at Omaha
ABSTRACT
Noticing that unsupervised clustering may produce clusters that are irrelevant to the research hypotheses and interests, we generalize traditional unsupervised clustering into semi-supervised clustering based on our previously proposed Message Passing Clustering (MPC). In the semi-supervised MPC, prior knowledge such as instance-level and attribute-level constraints are used to guide the clustering process towards better and interpretable partitions. We applied the unsupervised MPC (null background) to phylogenetic analysis of Mycobacterium and the semi-supervised MPC to colon cancer microarray data analysis. The results show that MPC is superior to the widely accepted neighbor-joining and hierarchical clustering methods, and the semi-supervised MPC is even more powerful in biological data analysis such as gene selection and cancer diagnosis using microarray.
INDEX TERMS
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CITATION

D. Bastola, X. Deng, H. Geng and H. Ali, "On Clustering Biological Data Using Unsupervised and Semi-Supervised Message Passing," 13th IEEE International Conference on BioInformatics and BioEngineering(BIBE), Minneapolis, Minnesota, 2005, pp. 294-298.
doi:10.1109/BIBE.2005.44
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