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International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining

TSDM 2000: Temporal, Spatial, and Spatio-Temporal Data Mining pp 133–146Cite as

AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2007))

Abstract

Wide spread clustering algorithms use the Euclidean distance to measure spatial proximity. However, obstacles in other GIS data-layers prevent traversing the straight path between two points. AUTOCLUST+ clusters points in the presence of obstacles based on Voronoi modeling and Delaunay Diagrams. The algorithm is free of user-supplied arguments and incorporates global and local variations. Thus, it detects high-quality clusters (clusters of arbitrary shapes, clusters of different densities, sparse clusters adjacent to high-density clusters, multiple bridges between clusters and closely located high-density clusters) without prior knowledge. Consequently, it successfully supports correlation analyses between layers (requiring high-quality clusters) and more general locational optimization problems in the presence of obstacles. All this within O(n log n+[m+R] log n) expected time, where n is the number of data points, m is the number of line-segments that determine the obstacles and R is the number of Delaunay edges intersecting some obstacles. A series of detailed performance evaluations illustrates the power of AUTOCLUST+ and confirms the virtues of our approach.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Software Engineering, The University of Newcastle, NSW 2308, Callaghan, Australia

    Vladimir Estivill-Castro & Ickjai Lee

Authors
  1. Vladimir Estivill-Castro
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  2. Ickjai Lee
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Editor information

Editors and Affiliations

  1. School of Informatics and Engineering, Flinders University of South Australia, P.O. Box 2100, 5001, Adelaide, South Australia

    John F. Roddick

  2. National Center for Geographic Information and Analysis, University of Maine, ME 04469-5711, Maine, Orono, USA

    Kathleen Hornsby

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© 2001 Springer-Verlag Berlin Heidelberg

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Estivill-Castro, V., Lee, I. (2001). AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles. In: Roddick, J.F., Hornsby, K. (eds) Temporal, Spatial, and Spatio-Temporal Data Mining. TSDM 2000. Lecture Notes in Computer Science(), vol 2007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45244-3_11

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  • DOI: https://doi.org/10.1007/3-540-45244-3_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41773-6

  • Online ISBN: 978-3-540-45244-7

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