Name
Title | ||
|---|---|---|
A new clustering algorithm using message passing and its applications in analyzing microarray data |
Abstract | ||
|---|---|---|
In this paper, we proposed a new clustering algorithm that employs the concept of message passing to describe parallel and spontaneous biological processes. Inspired by real-life situations in which people in large gatherings form groups by exchanging messages, message passing clustering (MPC) allows data objects to communicate with each other and produces clusters in parallel, thereby making the clustering process intrinsic and improving the clustering performance. We have proved that MPC shares similarity with hierarchical clustering but offers significantly improved performance because it takes into account both local and global structure. MPC can be easily implemented in a parallel computing platform for the purpose of speed-up. To validate the MPC method, we applied MPC to microarray data from the Stanford yeast cell-cycle database. The results show that MPC gave better clustering solutions in terms of homogeneity and separation values than other clustering methods. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICMLA.2005.3 | ICMLA |
Keywords | Field | DocType |
parallel processing,stanford yeast cell-cycle database,clustering performance,new clustering algorithm,pattern clustering,microarray data analysis,clustering process,genetics,parallel computing platform,message passing clustering,clustering method,message exchange,parallel biological process,biology computing,mpc shares similarity,clustering solution,analyzing microarray data,message passing,data object,mpc method,spontaneous biological process,hierarchical clustering,cell cycle,microarray data,biological process,parallel computer | Fuzzy clustering,Canopy clustering algorithm,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Affinity propagation,Correlation clustering,Computer science,Theoretical computer science,Artificial intelligence,Cluster analysis,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2495-8 | 6 | 0.48 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huimin Geng | 1 | 37 | 7.02 |
| Xutao Deng | 2 | 86 | 8.22 |
| Hesham Ali | 3 | 29 | 5.18 |