A new clustering algorithm using message passing and its applications in analyzing microarray data

Huimin Geng, Deng Xutao, Hesham H Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationProceedings - ICMLA 2005
Subtitle of host publicationFourth International Conference on Machine Learning and Applications
Pages145-150
Number of pages6
DOIs
StatePublished - Dec 1 2005
EventICMLA 2005: 4th International Conference on Machine Learning and Applications - Los Angeles, CA, United States
Duration: Dec 15 2005Dec 17 2005

Publication series

NameProceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications
Volume2005

Conference

ConferenceICMLA 2005: 4th International Conference on Machine Learning and Applications
CountryUnited States
CityLos Angeles, CA
Period12/15/0512/17/05

Fingerprint

Message passing
Microarrays
Clustering algorithms
Parallel processing systems
Yeast
Cells

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Geng, H., Xutao, D., & Ali, H. H. (2005). A new clustering algorithm using message passing and its applications in analyzing microarray data. In Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications (pp. 145-150). [1607443] (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications; Vol. 2005). https://doi.org/10.1109/ICMLA.2005.3

A new clustering algorithm using message passing and its applications in analyzing microarray data. / Geng, Huimin; Xutao, Deng; Ali, Hesham H.

Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications. 2005. p. 145-150 1607443 (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Geng, H, Xutao, D & Ali, HH 2005, A new clustering algorithm using message passing and its applications in analyzing microarray data. in Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications., 1607443, Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications, vol. 2005, pp. 145-150, ICMLA 2005: 4th International Conference on Machine Learning and Applications, Los Angeles, CA, United States, 12/15/05. https://doi.org/10.1109/ICMLA.2005.3
Geng H, Xutao D, Ali HH. A new clustering algorithm using message passing and its applications in analyzing microarray data. In Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications. 2005. p. 145-150. 1607443. (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications). https://doi.org/10.1109/ICMLA.2005.3
Geng, Huimin ; Xutao, Deng ; Ali, Hesham H. / A new clustering algorithm using message passing and its applications in analyzing microarray data. Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications. 2005. pp. 145-150 (Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications).
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