Name
Title | ||
|---|---|---|
Message Passing Clustering (MPC): a knowledge-based framework for clustering under biological constraints. |
Abstract | ||
|---|---|---|
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 objects to communicate with each other. MPC also provides an extensible framework to accommodate additional features into clustering, such 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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1504/IJDMB.2008.019092 | IJDMB |
Keywords | DocType | Volume |
new clustering algorithm,knowledge-based framework,extensive experiment,message passing clustering,popular clustering algorithm,adaptive feature weight,additional feature,extensible framework,data object,phylogenetic data,spontaneous clustering process,biological constraint | Journal | 2 |
Issue | ISSN | Citations |
2 | 1748-5673 | 7 |
PageRank | References | Authors |
0.55 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huimin Geng | 1 | 37 | 7.02 |
| Xutao Deng | 2 | 86 | 8.22 |
| Hesham H. Ali | 3 | 276 | 47.48 |