Tree-based clustering for gene expression data

Authors:
Baoying Wang;William Perrizo
Affiliations:
North Dakota State University, Fargo, ND;North Dakota State University, Fargo, ND
Venue:
Proceedings of the 2005 ACM symposium on Applied computing
Year:
2005

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BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data

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Abstract

Data clustering methods have been proven to be a successful data mining technique in analysis of gene expression data and many other types of data. However, some concerns and challenges still remain, e.g., in gene expression clustering. In this paper, we propose an efficient clustering method using attractor trees. The combination of the density-based approach and the similarity-based approach considers clusters with diverse shapes, densities, and sizes. Experiments on gene expression datasets demonstrate that our approach is efficient and scalable with competitive accuracy.