Discovery of Interesting Regions in Spatial Data Sets Using Supervised Clustering

  • Christoph F. Eick
  • Banafsheh Vaezian
  • Dan Jiang
  • Jing Wang
Conference paper

DOI: 10.1007/11871637_16

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)
Cite this paper as:
Eick C.F., Vaezian B., Jiang D., Wang J. (2006) Discovery of Interesting Regions in Spatial Data Sets Using Supervised Clustering. In: Fürnkranz J., Scheffer T., Spiliopoulou M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science, vol 4213. Springer, Berlin, Heidelberg

Abstract

The discovery of interesting regions in spatial datasets is an important data mining task. In particular, we are interested in identifying disjoint, contiguous regions that are unusual with respect to the distribution of a given class; i.e. a region that contains an unusually low or high number of instances of a particular class. This paper centers on the discussion of techniques, methodologies, and algorithms to discover such regions. A measure of interestingness and a supervised clustering framework are introduced for this purpose. Moreover, three supervised clustering algorithms are proposed in the paper: an agglomerative hierarchical supervised clustering named SCAH, an agglomerative, grid-based clustering method named SCHG, and lastly an algorithm named SCMRG which searches a multi-resolution grid structure top down for interesting regions. Finally, experimental results of applying the proposed framework and algorithms to the problem of identifying hotspots in spatial datasets are discussed.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christoph F. Eick
    • 1
  • Banafsheh Vaezian
    • 1
  • Dan Jiang
    • 1
  • Jing Wang
    • 1
  1. 1.Department of Computer ScienceUniversity of HoustonHoustonU.S.A

Personalised recommendations