Prediction mining - An approach to mining association rules for prediction

Jitender Deogun, Liying Jiang

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

20 Scopus citations

Abstract

An interesting application of association mining in the context temporal databases is that of prediction. Prediction is to use the antecedent of a rule to predict the consequent of the rule. But not all of association rules may be suitable for prediction. In this paper, we investigate the properties of rules for prediction, and develop an approach called prediction mining -mining a set of association rules that are useful for prediction. Prediction mining discovers a set of prediction rules that have three properties. First, there must be a time lag between antecedent and consequent of the rule. Second, antecedent of a prediction rule is the minimum condition that implies the consequent. Third, a prediction rule must have relatively stable confidence with respect to the time frame determined by application domain. We develop a prediction mining algorithm for discovering the set of prediction rules. The efficiency and effectiveness of our approach is validated by experiments on both synthetic and real-life databases, we show that the prediction mining approach efficiently discovers a set of rules that are proper for prediction.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages98-108
Number of pages11
DOIs
Publication statusPublished - Dec 1 2005
Event10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005 - Regina, Canada
Duration: Aug 31 2005Sep 3 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3642 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005
CountryCanada
CityRegina
Period8/31/059/3/05

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Deogun, J., & Jiang, L. (2005). Prediction mining - An approach to mining association rules for prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 98-108). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3642 LNAI). https://doi.org/10.1007/11548706_11