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A Novel Approach for Effective Learning of Cluster Structures with Biological Data Applications

  • Miyoung Shin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4316)

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.

Keywords

Synthetic Data Cluster Structure Synthetic Dataset Effective Learn Adjusted Rand Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miyoung Shin
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceKyungpook National UniversityDaeguKorea

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