Abstract
Recently DNA microarray gene expression studies have been actively performed for mining unknown biological knowledge hidden under a large volume of gene expression data in a systematic way. In particular, the problem of finding groups of co-expressed genes or samples has been largely investigated due to its usefulness in characterizing unknown gene functions or performing more sophisticated tasks, such as modeling biological pathways. Nevertheless, there are still some difficulties in practice to identify good clusters since many clustering methods require user’s arbitrary selection of the number of target clusters. In this paper we propose a novel approach to systematically identifying good candidates of cluster numbers so that we can minimize the arbitrariness in cluster generation. Our experimental results on both synthetic dataset and real gene expression dataset show the applicability and usefulness of this approach in microarray data mining.
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© 2006 Springer-Verlag Berlin Heidelberg
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Shin, M. (2006). A Novel Approach for Effective Learning of Cluster Structures with Biological Data Applications. In: Dalkilic, M.M., Kim, S., Yang, J. (eds) Data Mining and Bioinformatics. VDMB 2006. Lecture Notes in Computer Science(), vol 4316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11960669_2
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DOI: https://doi.org/10.1007/11960669_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68970-6
Online ISBN: 978-3-540-68971-3
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