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SUBic: A Scalable Unsupervised Framework for Discovering High Quality Biclusters

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Abstract

A biclustering algorithm extends conventional clustering techniques to extract all of the meaningful subgroups of genes and conditions in the expression matrix of a microarray dataset. However, such algorithms are very sensitive to input parameters and show poor scalability. This paper proposes a scalable unsupervised biclustering framework, SUBic, to find high quality constant-row biclusters in an expression matrix effectively. A one-dimensional clustering algorithm is proposed to partition the attributes, that is, columns of an expression matrix into disjoint groups based on the similarity of expression values. These groups form a set of short transactions and are used to discover a set of frequent itemsets each of which corresponds to a bicluster. However, a bicluster may include any attribute whose expression value is not similar enough to others, so a bicluster refinement is used to enhance the quality of a bicluster by removing those attributes based on its distribution of expression values. The performance of the proposed method is comparatively analyzed through a series of experiments on synthetic and real datasets.

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Authors and Affiliations

  1. Department of Computer Science, Yonsei University, Seoul, 120-749, Korea

    Jooil Lee, Yanhua Jin & Won Suk Lee

Authors
  1. Jooil Lee
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  2. Yanhua Jin
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  3. Won Suk Lee
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Corresponding author

Correspondence to Won Suk Lee.

Additional information

This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) of Korea under Grant No. 2011–0016648.

The preliminary version of the paper was published in the Proceedings of EDB2012.

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Lee, J., Jin, Y. & Lee, W.S. SUBic: A Scalable Unsupervised Framework for Discovering High Quality Biclusters. J. Comput. Sci. Technol. 28, 636–646 (2013). https://doi.org/10.1007/s11390-013-1364-y

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  • DOI: https://doi.org/10.1007/s11390-013-1364-y

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