Spatial Data Mining

Advances in Spatial Databases

Volume 951 of the series Lecture Notes in Computer Science pp 47-66

Date:

Discovery of spatial association rules in geographic information databases

  • Krzysztof KoperskiAffiliated withSchool of Computing Science, Simon Fraser University
  • , Jiawei HanAffiliated withSchool of Computing Science, Simon Fraser University

Abstract

Spatial data mining, i.e., discovery of interesting, implicit knowledge in spatial databases, is an important task for understanding and use of spatial data- and knowledge-bases. In this paper, an efficient method for mining strong spatial association rules in geographic information databases is proposed and studied. A spatial association rule is a rule indicating certain association relationship among a set of spatial and possibly some nonspatial predicates. A strong rule indicates that the patterns in the rule have relatively frequent occurrences in the database and strong implication relationships. Several optimization techniques are explored, including a two-step spatial computation technique (approximate computation on large sets, and refined computations on small promising patterns), shared processing in the derivation of large predicates at multiple concept levels, etc. Our analysis shows that interesting association rules can be discovered efficiently in large spatial databases.