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    Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach

    Nascimento, S. and Felizardo, R. and Mirkin, Boris (2013) Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach. Expert Systems , ISSN 0266-4720.

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    Abstract

    This paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, Fuzzy Additive Spectral clustering (FADDIS), recently introduced by authors. Specifically, we extend our analysis to ‘difficult’ data structures from the recent literature and develop two synthetic data generators simulating affinity data of Gaussian clusters and genuine additive similarity data, with a controlled level of noise. The FADDIS is experimentally verified on these data in comparison with two state-of-the-art fuzzy clustering methods. The claimed ability of FADDIS to help in determining the right number of clusters is experimentally tested, and the role of the pseudo-inverse Laplacian data transformation in this is highlighted. A potentially useful extension of the method to biclustering is introduced.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Relational fuzzy clustering, spectral fuzzy clustering, Laplacian normalization, number of clusters
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Structural Molecular Biology, Institute of (ISMB)
    Depositing User: Administrator
    Date Deposited: 07 Jun 2013 07:11
    Last Modified: 06 Dec 2016 10:33
    URI: http://eprints.bbk.ac.uk/id/eprint/7345

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