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    Clustering by pairwise similarity

    241225_Li Qilin 2016.pdf (5.461Mb)
    Access Status
    Open access
    Authors
    Li, Qilin
    Date
    2016
    Supervisor
    Assoc. Prof. Wan-Quan Liu
    Assoc. Prof. Ling Li
    Type
    Thesis
    Award
    MPhil
    
    Metadata
    Show full item record
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/1939
    Collection
    • Curtin Theses
    Abstract

    We proposed two novel clustering approaches, AFS and AFSSC, to address the problems in image clustering, semantic learning and manifold learning, respectively. By applying fuzzy membership function for data representation and similarity measure in AFS clustering, the pairwise affinity relationship is revealed more clearly than the commonly used Euclidean distance. In AFSSC method, we formulate AFS for similarity matrix learning and map the matrix to a weighted graph in which the clustering problem can be solved by graph cut theory.

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