TY - GEN
T1 - A new clustering algorithm using message passing and its applications in analyzing microarray data
AU - Geng, Huimin
AU - Xutao, Deng
AU - Ali, Hesham
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33847287400&partnerID=8YFLogxK
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U2 - 10.1109/ICMLA.2005.3
DO - 10.1109/ICMLA.2005.3
M3 - Conference contribution
AN - SCOPUS:33847287400
SN - 0769524958
SN - 9780769524955
T3 - Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications
SP - 145
EP - 150
BT - Proceedings - ICMLA 2005
T2 - ICMLA 2005: 4th International Conference on Machine Learning and Applications
Y2 - 15 December 2005 through 17 December 2005
ER -