Discovery of spatial association rules in geographic information databases

Krzysztof Koperski, Jiawei Han

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

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. Severed 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.

Original languageEnglish (US)
Title of host publicationAdvances in Spatial Databases - 4th International Symposium, SSD 1995, Proceedings
EditorsJohn R. Herring, Max J. Egenhofer
PublisherSpringer-Verlag
Pages47-66
Number of pages20
ISBN (Print)3540601597, 9783540601593
DOIs
StatePublished - Jan 1 1995
Externally publishedYes
Event4th International Symposium on Large Spatial Databases, SSD 1995 - Portland, United States
Duration: Aug 6 1995Aug 9 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume951
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Symposium on Large Spatial Databases, SSD 1995
CountryUnited States
CityPortland
Period8/6/958/9/95

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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