Subspace clustering for microarray data analysis: Multiple criteria and significance assessment

Hui Fang; Chengxiang Zhai; Lei Liu; Jiong Yang

Subspace clustering for microarray data analysis: Multiple criteria and significance assessment

Hui Fang, Chengxiang Zhai, Lei Liu, Jiong Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A new clustering method for analyzing microarray data was proposed. The depth of the common parent nodes shared by the genes in a cluster to assess the quality of the cluster was used. The algorithm aims at discovering nontraditional subspace clusters. The proposed method has a great potential for helping biologists discover meaningful gene clusters through generating more coherent clusters and ranking clusters based on statistical significance.

Original language	English (US)
Title of host publication	Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004
Pages	582-583
Number of pages	2
State	Published - 2004
Event	Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 - Stanford, CA, United States Duration: Aug 16 2004 → Aug 19 2004

Publication series

Name	Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004

Other

Other	Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004
Country/Territory	United States
City	Stanford, CA
Period	8/16/04 → 8/19/04

ASJC Scopus subject areas

General Engineering

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Subspace clustering for microarray data analysis: Multiple criteria and significance assessment. / Fang, Hui; Zhai, Chengxiang; Liu, Lei et al.
Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004. 2004. p. 582-583 (Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fang, H, Zhai, C, Liu, L & Yang, J 2004, Subspace clustering for microarray data analysis: Multiple criteria and significance assessment. in Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004. Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004, pp. 582-583, Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004, Stanford, CA, United States, 8/16/04.

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title = "Subspace clustering for microarray data analysis: Multiple criteria and significance assessment",

abstract = "A new clustering method for analyzing microarray data was proposed. The depth of the common parent nodes shared by the genes in a cluster to assess the quality of the cluster was used. The algorithm aims at discovering nontraditional subspace clusters. The proposed method has a great potential for helping biologists discover meaningful gene clusters through generating more coherent clusters and ranking clusters based on statistical significance.",

author = "Hui Fang and Chengxiang Zhai and Lei Liu and Jiong Yang",

year = "2004",

language = "English (US)",

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note = "Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004 ; Conference date: 16-08-2004 Through 19-08-2004",

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T2 - Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004

AU - Fang, Hui

AU - Zhai, Chengxiang

AU - Liu, Lei

AU - Yang, Jiong

PY - 2004

Y1 - 2004

N2 - A new clustering method for analyzing microarray data was proposed. The depth of the common parent nodes shared by the genes in a cluster to assess the quality of the cluster was used. The algorithm aims at discovering nontraditional subspace clusters. The proposed method has a great potential for helping biologists discover meaningful gene clusters through generating more coherent clusters and ranking clusters based on statistical significance.

AB - A new clustering method for analyzing microarray data was proposed. The depth of the common parent nodes shared by the genes in a cluster to assess the quality of the cluster was used. The algorithm aims at discovering nontraditional subspace clusters. The proposed method has a great potential for helping biologists discover meaningful gene clusters through generating more coherent clusters and ranking clusters based on statistical significance.

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M3 - Conference contribution

AN - SCOPUS:14044262257

SN - 0769521940

SN - 9780769521947

T3 - Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004

SP - 582

EP - 583

BT - Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004

Y2 - 16 August 2004 through 19 August 2004

ER -

Subspace clustering for microarray data analysis: Multiple criteria and significance assessment

Abstract

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