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

Authors:
Shirlly C. M. Silva;Daniel S. A. de Araujo;Raul B. Paradeda;Valmar S. Severiano-Sobrinho;Marcilio C. P. de Souto
Affiliations:
Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, RN, Brazil;Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, RN, Brazil;Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, RN, Brazil;Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, RN, Brazil;Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, RN, Brazil
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 5
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Abstract

Exploratory data analysis and, in particular, data clustering can significantly benefit from combining multiple data partitions – cluster ensemble. In this context, we analyze the potential of applying cluster ensemble techniques to gene expression microarray data. Our experimental results show that there is often a significant improvement in the results obtained with the use of ensemble techniques when compared to those based on the clustering techniques used individually.