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  • A Hough Transform Based Biclustering Algorithm for Gene Expression Data

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    Author(s)
    To, C
    Nguyen, TT
    Liew, AWC
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Nguyen, Tien Thanh T.
    Chieu To, Cuong
    Year published
    2014
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    Abstract
    In pattern classification, when the feature space is of high dimension or patterns are "similar" on a subset of features only, the traditional clustering methods do not have good performances. Biclustering is a class of methods that simultaneously group 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. and oOne of the most complex models in biclustering problem is linear coherent model. So sSeveral biclustering ...
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    In pattern classification, when the feature space is of high dimension or patterns are "similar" on a subset of features only, the traditional clustering methods do not have good performances. Biclustering is a class of methods that simultaneously group 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. and oOne of the most complex models in biclustering problem is linear coherent model. So sSeveral 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 and apply it to gene expression data.
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    Conference Title
    Communications in Computer and Information Science
    Volume
    481
    Publisher URI
    http://www.icmlc.com/ICMLC/formerICMLC_2014.html
    DOI
    https://doi.org/10.1007/978-3-662-45652-1_11
    Copyright Statement
    © 2014 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/66985
    Collection
    • Conference outputs

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