Evaluation of the contents of partitions obtained with clustering gene expression data

Authors:
Katti Faceli;André C. P. L. F. de Carvalho;Marcílio C. P. de Souto
Affiliations:
Instituto de Ciências Matemáticas e de Computação, Departamento de Ciências de Computação e Estatística, Universidade de São Paulo, São Carlos, SP ...;Instituto de Ciências Matemáticas e de Computação, Departamento de Ciências de Computação e Estatística, Universidade de São Paulo, São Carlos, SP ...;Departamento de Informática e Matemática Aplicada – DIMAp, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil
Venue:
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
Year:
2005

Citing 4
Cited 1

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Algorithms for clustering data
Cluster validity methods: part I

ACM SIGMOD Record
Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

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Cluster Analysis for Gene Expression Data: A Survey

IEEE Transactions on Knowledge and Data Engineering

Individual clustering and homogeneous cluster ensemble approaches applied to gene expression data

AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence

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Abstract

This work investigates the behavior of two different clustering algorithms, with two proximity measures, in terms of the contents of the partitions obtained with them. An analysis of how the classes are separated by these algorithms, as different numbers of clusters are generated, is also presented. A discussion on the use of these information in the identification of special cases for further analysis by biologists is presented.