Discovering partial periodic sequential association rules with time lag in multiple sequences for prediction

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

16 Scopus citations

Abstract

A periodic pattern indicates something persistent and predictable, so it is important to identify and characterize the periodicity. This paper presents an approach for mining partial periodic association rules in temporal databases. This approach allows the discovery of periodic episodes such that the events in an episode are not limited to a fixed order. Moreover, this approach treats the antecedent and consequent of a rule separately and allows time lag between them. Thus, rules discovered are useful in many applications for prediction. The approach is implemented using two algorithms based on two data structures, event-based linked list and window-based linked list.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages332-341
Number of pages10
Publication statusPublished - Dec 1 2005
Event15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005 - Saratoga Springs, NY, United States
Duration: May 25 2005May 28 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3488 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005
CountryUnited States
CitySaratoga Springs, NY
Period5/25/055/28/05

    Fingerprint

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Li, D., & Deogun, J. S. (2005). Discovering partial periodic sequential association rules with time lag in multiple sequences for prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 332-341). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3488 LNAI).