Discovering sequential association rules with constraints and time lags in multiple sequences

Sherri K. Harms, Jitender Deogun, Tsegaye Tadesse

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

38 Scopus citations

Abstract

We present MOWCATL, an efficient method for mining frequent sequential association rules from multiple sequential data sets with a time lag between the occurrence of an antecedent sequence and the corresponding consequent sequence. This approach finds patterns in one or more sequences that precede the occurrence of patterns in other sequences, with respect to user-specified constraints. In addition to the traditional frequency and support constraints in sequential data mining, this approach uses separate antecedent and consequent inclusion constraints. Moreover, separate antecedent and consequent maximum window widths are used to specify the antecedent and consequent patterns that are separated by the maximum time lag. We use multiple time series drought risk management data to show that our approach can be effectively employed in real-life problems. The experimental results validate the superior performance of our method for efficiently finding relationships between global climatic episodes and local drought conditions. We also compare our new approach to existing methods and show how they complement each other to discover associations in a drought risk management decision support system.

Original languageEnglish (US)
Title of host publicationFoundations of Intelligent Systems - 13th International Symposium, ISMIS 2002, Proceedings
EditorsMohand-Said Hacid, Zbigniew W. Ras, Djamel A. Zighed, Yves Kodratoff
PublisherSpringer Verlag
Pages432-441
Number of pages10
ISBN (Print)3540437851, 9783540437857
DOIs
StatePublished - 2002
Event13th International Symposium on Methodologies for Intelligent Systems, ISMIS 2002 - Lyon, France
Duration: Jun 27 2002Jun 29 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2366 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Symposium on Methodologies for Intelligent Systems, ISMIS 2002
Country/TerritoryFrance
CityLyon
Period6/27/026/29/02

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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