Density-based clustering of polygons

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. PDBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate their topological and spatial properties in the process of clustering by using a distance function customized for the polygon space. The objective of our clustering algorithm is to produce spatially compact clusters. We measure the compactness of the clusters produced using P-DBSCAN and compare it with the clusters formed using DBSCAN, using the Schwartzberg Index. We measure the effectiveness and robustness of our algorithm using a synthetic dataset and two real datasets. Results show that the clusters produced using P-DBSCAN have a lower compactness index (hence more compact) than DBSCAN.

Original languageEnglish (US)
Title of host publication2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
Pages171-178
Number of pages8
DOIs
StatePublished - Jul 20 2009
Event2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Nashville, TN, United States
Duration: Mar 30 2009Apr 2 2009

Publication series

Name2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings

Conference

Conference2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009
CountryUnited States
CityNashville, TN
Period3/30/094/2/09

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software

Cite this

Joshi, D., Samal, A. K., & Soh, L-K. (2009). Density-based clustering of polygons. In 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings (pp. 171-178). [4938646] (2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings). https://doi.org/10.1109/CIDM.2009.4938646