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  • Polygonization of Point Clusters through Cluster Boundary Extraction for Geographical Data Mining

    Author(s)
    Lee, I
    Estivill-Castro, V
    Griffith University Author(s)
    Estivill-Castro, Vladimir
    Year published
    2002
    Metadata
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    Abstract
    Interpretability and usability of clustering results are of fundamental importance. A linear time method for transforming point clusters into polygons is explored. This method automatically translates a point data layer into a space filling layer where clusters are identified as some of the resulting regions. The method is based on robustly identifying cluster boundaries in point data. The cluster polygonization process analyses the distribution of intra-cluster edges and the distribution of inter-cluster edges in Delaunay Triangulations. It approximates shapes of clusters and suggests polygons of clusters. The method ...
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    Interpretability and usability of clustering results are of fundamental importance. A linear time method for transforming point clusters into polygons is explored. This method automatically translates a point data layer into a space filling layer where clusters are identified as some of the resulting regions. The method is based on robustly identifying cluster boundaries in point data. The cluster polygonization process analyses the distribution of intra-cluster edges and the distribution of inter-cluster edges in Delaunay Triangulations. It approximates shapes of clusters and suggests polygons of clusters. The method can then be applied to display choropleth maps of point data without a reference map or to identify associations in the spatial dimension for geographical data mining.
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    Conference Title
    ADVANCES IN SPATIAL DATA HANDLING
    DOI
    https://doi.org/10.1007/978-3-642-56094-1_3
    Publication URI
    http://hdl.handle.net/10072/22848
    Collection
    • Conference outputs

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