Spatio-temporal association mining for un-sampled sites

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


In this paper, we investigate interpolation methods that are suitable for discovering spatio-temporal association rules for unsampled points with an initial focus on drought risk management. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for unsampled sites, interpolation methods can be applied at any stage of this data mining process. We develop and integrate three interpolation models into our association rule mining algorithm. The performance of these three models is experimentally evaluated comparing interpolated association rules with rules discovered from actual raw data.

Original languageEnglish (US)
Title of host publicationFoundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings
EditorsNing Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki
PublisherSpringer Verlag
Number of pages8
ISBN (Print)3540202560, 9783540202561
StatePublished - 2003
Event14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003 - Maebashi City, Japan
Duration: Oct 28 2003Oct 31 2003

Publication series

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


Conference14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003
CityMaebashi City

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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