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Fuzzy Knowledge-Based Subspace Clustering for Life Science Data Analysis

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Knowledge-Based Systems in Biomedicine and Computational Life Science

Part of the book series: Studies in Computational Intelligence ((SCI,volume 450))

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Abstract

Features or attributes play an important role when handling multi-dimensional datasets. Generally, not all the features are needed to find several groups of similar objects in traditional clustering methods because some of the features may not be relevant and also redundant. Hence, the concept of identifying subsets of the features that are relevant to clusters is introduced, instead of using the full set of features. This chapter discusses the use of the prior knowledge of the importance of features and their interaction in constructing both fuzzy measures and signed fuzzy measures for subspace clustering. The Choquet integral, which is known as a useful aggregation operator with respect to fuzzy measure, is used to aggregate the importance and interaction of the features. The concept of fuzzy knowledge-based subspace clustering is applied especially to the analysis of life science data in this chapter.

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Authors and Affiliations

  1. School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia

    Theam Foo Ng, Xiuping Jia & Donald Fraser

  2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia

    Theam Foo Ng

  3. Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu, Fukushima, 965-8580, Japan

    Tuan D. Pham

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  1. Theam Foo Ng
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  1. Aizu Research Cluster for Medical Eng, and Informatics Center for Advanced, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan

    Tuan D. Pham

  2. , Adjunct Professor, University of Canberra, Kirinari St. Mail Room, ACT, 2601, Australia

    Lakhmi C. Jain

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Ng, T.F., Pham, T.D., Jia, X., Fraser, D. (2013). Fuzzy Knowledge-Based Subspace Clustering for Life Science Data Analysis. In: Pham, T., Jain, L. (eds) Knowledge-Based Systems in Biomedicine and Computational Life Science. Studies in Computational Intelligence, vol 450. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33015-5_8

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