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Feature Group Weighting and Topological Biclustering

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8835))

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Abstract

This paper proposes a new method to weight feature groups for biclustering. In this method, the observations and features are divided into biclusters, based on their characteristics. The weights are introduced to the biclustering process to simultaneously identify the relevance of feature groups in each bicluster. A new biclustering algorithm wBiTM (Weighted Biclustering Topological Map) is proposed. The new method is an extension to self-organizing map algorithm by adding the weight parameter and a new prototype for bicluster. Experimental results on synthetic data show the properties of the weights in wBiTM.

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Author information

Authors and Affiliations

  1. CNRS LIPN-UMR 7030, Paris 13 University, Sorbonne Paris City, 99, av. J-B Clement, 93430, Villetaneuse, France

    Tugdual Sarazin, Mustapha Lebbah, Hanane Azzag & Amine Chaibi

  2. ALTIC, 25 rue Bergre, 75009, Paris

    Tugdual Sarazin

Authors
  1. Tugdual Sarazin
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  2. Mustapha Lebbah
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  3. Hanane Azzag
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  4. Amine Chaibi
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St., 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Sarazin, T., Lebbah, M., Azzag, H., Chaibi, A. (2014). Feature Group Weighting and Topological Biclustering. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_45

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  • DOI: https://doi.org/10.1007/978-3-319-12640-1_45

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12639-5

  • Online ISBN: 978-3-319-12640-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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