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A Hough Transform-Based Biclustering Algorithm for Gene Expression Data

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Machine Learning and Cybernetics (ICMLC 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 481))

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Abstract

In pattern classification, when the feature space is of high dimensionality or patterns are “similar” on a subset of features only, the traditional clustering methods do not show good performance. Biclustering is a class of methods that simultaneously carry out grouping on two dimensions and has many applications to different fields, especially gene expression data analysis. Because of simultaneous classification on both rows and columns of a data matrix, the biclustering problem is inherently intractable and computationally complex. One of the most complex models in biclustering problem is linear coherent model. Several biclustering algorithms based on this model have been proposed in recent years. However, none of them is able to perfectly recognize all linear patterns in a bicluster. In this work, we propose a novel algorithm based on Hough transform that can find all linear coherent patterns. In the sequel we apply it to gene expression data.

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Authors and Affiliations

  1. School of Information and Communication Technology, Griffith University, Logan, Australia

    Cuong To, Tien Thanh Nguyen & Alan Wee-Chung Liew

Authors
  1. Cuong To
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  2. Tien Thanh Nguyen
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  3. Alan Wee-Chung Liew
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Corresponding author

Correspondence to Alan Wee-Chung Liew .

Editor information

Editors and Affiliations

  1. Hebei University, Baoding, China

    Xizhao Wang

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. South China University of Technology, Guangzhou, China

    Patrick Chan

  4. Hebei University, Baoding, China

    Qiang He

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To, C., Nguyen, T.T., Liew, A.WC. (2014). A Hough Transform-Based Biclustering Algorithm for Gene Expression Data. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_11

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  • DOI: https://doi.org/10.1007/978-3-662-45652-1_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45651-4

  • Online ISBN: 978-3-662-45652-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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