Fast mining of spatial collocations

  • Authors:
  • Xin Zhang;Nikos Mamoulis;David W. Cheung;Yutao Shou

  • Affiliations:
  • The University of Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong;The University of Hong Kong, Hong Kong

  • Venue:
  • Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

Spatial collocation patterns associate the co-existence of non-spatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs with certain deceases in their spatial neighborhood. Previous work on discovering collocation patterns converts neighborhoods of feature instances to itemsets and applies mining techniques for transactional data to discover the patterns. We propose a method that combines the discovery of spatial neighborhoods with the mining process. Our technique is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. As demonstrated by experimentation, it yields significant performance improvements compared to previous approaches.