Geospatial Cluster Tessellation through the Complete Order-k Voronoi diagrams

Ickjai Lee, Reece Pershouse, Kyungmi Lee

    Research output: Book chapter/Published conference paperConference paperpeer-review

    3 Citations (Scopus)


    In this paper, we propose a postclustering process that robustly computes cluster regions at different levels of granularity through the complete Order-k Voronoi diagrams. The robustness and flexibility of the proposed method overcome the application-dependency and rigidity of traditional approaches. The proposed cluster tessellation method robustly models monotonic and nonmonotonic cluster growth, and provides fuzzy membership in order to represent indeterminacy of cluster regions. It enables the user to explore cluster structures hidden in a dataset in various scenarios and supports for 'what-if' and 'what-happen' analysis. Tessellated clusters can be effectively used for cluster reasoning and concept learning.
    Original languageEnglish
    Title of host publicationCOSIT
    Subtitle of host publicationConference on Spatial Information Theory 2007
    EditorsB. Kuipers
    Place of PublicationBerlin, German
    PublisherSpringer-Verlag London Ltd.
    Number of pages16
    Publication statusPublished - 2007
    EventConference on Spatial Information Theory - Melbourne, Australia, Australia
    Duration: 19 Sept 200723 Sept 2007


    ConferenceConference on Spatial Information Theory


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