Mining time-profiled associations: An extended abstract

Jin Soung Yoo, Pusheng Zhang, Shashi Shekhar

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

3 Scopus citations

Abstract

A time-profiled association is an association pattern consistent with a query sequence over time, e.g., identifying the interacting relationship of droughts and wild fires in Australia with the El Nino phenomenon in the past 50 years. Traditional association rule mining approaches reveal the generic dependency among variables in association patterns but do not capture the evolution of these patterns over time. Incorporating the temporal evolution of association patterns and identifying the co-occurring patterns consistent over time can be done by time-profiled association mining. Mining time-profiled associations is computationally challenging due to the large size of the itemset space and the long time points in practice. In this paper, we propose a novel one-step algorithm to unify the generation of statistical parameter sequences and sequence retrieval. The proposed algorithm substantially reduces the itemset search space by pruning candidate itemsets based on the monotone property of the lower bounding measure of the sequence of statistical parameters. Experimental results show that our algorithm outperforms a naive approach.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages136-142
Number of pages7
StatePublished - Dec 1 2005
Event9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 - Hanoi, Viet Nam
Duration: May 18 2005May 20 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3518 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005
CountryViet Nam
CityHanoi
Period5/18/055/20/05

Fingerprint Dive into the research topics of 'Mining time-profiled associations: An extended abstract'. Together they form a unique fingerprint.

  • Cite this

    Yoo, J. S., Zhang, P., & Shekhar, S. (2005). Mining time-profiled associations: An extended abstract. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 136-142). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3518 LNAI).