Spatio-temporal association mining for un-sampled sites

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

Abstract

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
EditorsZbigniew W. Ras, Einoshin Suzuki, Ning Zhong, Shusaku Tsumoto
PublisherSpringer Verlag
Pages478-485
Number of pages8
ISBN (Print)3540202560, 9783540202561
Publication statusPublished - Jan 1 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)
Volume2871
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003
CountryJapan
CityMaebashi City
Period10/28/0310/31/03

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Li, D., & Deogun, J. (2003). Spatio-temporal association mining for un-sampled sites. In Z. W. Ras, E. Suzuki, N. Zhong, & S. Tsumoto (Eds.), Foundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings (pp. 478-485). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2871). Springer Verlag.