Article

gTRICLUSTER: a more general and effective 3d clustering algorithm for gene-sample-time microarray data

Authors:

Haoliang Jiang,

Shuigeng Zhou,

Jihong Guan,

Ying ZhengAuthors Info & Claims

BioDM'06: Proceedings of the 2006 international conference on Data Mining for Biomedical Applications

Pages 48 - 59

https://doi.org/10.1007/11691730_6

Published: 09 April 2006 Publication History

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Abstract

Clustering is an important technique in microarray data analysis, and mining three-dimensional (3D) clusters in gene-sample-time (simply GST) microarray data is emerging as a hot research topic in this area. A 3D cluster consists of a subset of genes that are coherent on a subset of samples along a segment of time series. This kind of coherent clusters may contain information for the users to identify useful phenotypes, potential genes related to these phenotypes and their expression rules. TRICLUSTER is the state-of-the-art 3D clustering algorithm for GST microarray data. In this paper, we propose a new algorithm to mine 3D clusters over GST microarray data. We term the new algorithm gTRICLUSTER because it is based on a more general 3D cluster model than the one that TRICLUSTER is based on. gTRICLUSTER can find more biologically meaningful coherent gene clusters than TRICLUSTER can do. It also outperforms TRICLUSTER in robustness to noise. Experimental results on a real-world microarray dataset validate the effectiveness of the proposed new algorithm.

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Published In

BioDM'06: Proceedings of the 2006 international conference on Data Mining for Biomedical Applications

April 2006

154 pages

ISBN:3540331042

Editors:
Jinyan Li
School of Computer Engineering, Nanyang Technological University, Singapore
,
Qiang Yang
School of Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, Singapore
,
Ah-Hwee Tan
Intelligent Systems Centre and School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Hong Kong, Singapore

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Springer-Verlag

Berlin, Heidelberg

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Published: 09 April 2006

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Gutierrez-Aviles DTorres JMartinez-Alvarez FCugliari JHong JPark J(2024)An evolutionary triclustering approach to discover electricity consumption patterns in FranceProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636034(386-394)Online publication date: 8-Apr-2024
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Tchagang AFamili FPan Y(2014)Subspace Clustering of DNA Microarray DataInternational Journal of Computational Models and Algorithms in Medicine10.4018/IJCMAM.20140701014:2(1-52)Online publication date: 1-Jul-2014
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Gutiérrez-Avilés DRubio-Escudero CMartínez-Álvarez FRiquelme J(2014)TriGenNeurocomputing10.1016/j.neucom.2013.03.061132(42-53)Online publication date: 1-May-2014
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Kremer HGünnemann SHeld ASeidl T(2012)Mining of temporal coherent subspace clusters in multivariate time series databasesProceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I10.1007/978-3-642-30217-6_37(444-455)Online publication date: 29-May-2012
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Peng WLi T(2011)Tensor clustering via adaptive subspace iterationIntelligent Data Analysis10.5555/2595479.259548315:5(695-713)Online publication date: 1-Sep-2011
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