Name
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/BIBE.2005.44 | BIBE |
Keywords | Field | DocType |
clustering process,traditional unsupervised clustering,semi-supervised mpc,semi-supervised message passing,cancer diagnosis,semi-supervised clustering,hierarchical clustering method,biological data analysis,unsupervised clustering,clustering biological data,colon cancer microarray data,unsupervised mpc,biological data,statistical analysis,genetics,hierarchical clustering,colon cancer,gene selection,microarray data analysis,cancer,message passing,neighbor joining | Fuzzy clustering,Data mining,Computer science,Artificial intelligence,Conceptual clustering,Cluster analysis,Single-linkage clustering,Hierarchical clustering,Canopy clustering algorithm,Clustering high-dimensional data,Correlation clustering,Bioinformatics,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2476-1 | 3 | 0.47 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huimin Geng | 1 | 37 | 7.02 |
| Xutao Deng | 2 | 86 | 8.22 |
| Dhundy Bastola | 3 | 110 | 17.73 |
| Hesham Ali | 4 | 29 | 5.18 |