Mining temporal association patterns under a similarity constraint

Jin Soung Yoo, Shashi Shekhar

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

17 Scopus citations

Abstract

We study the problem of mining all associated itemsets whose prevalence variations are similar to a given reference sequence from temporal databases. The discovered temporal association patterns can reveal interesting relationships of itemsets which co-occur with a particular event over time. A user-defined subset specification which consists of a reference sequence, a similarity function, and a dissimiliarty threshold is used for defining interesting temporal patterns and guiding the similarity search. We develop algorithms with exploring interesting properties for efficiently finding the similar temporal association patterns. Experimental results show that the proposed algorithms are efficient than a naive approach.

Original languageEnglish (US)
Title of host publicationScientific and Statistical Database Management - 20th International Conference, SSDBM 2008, Proceedings
Pages401-417
Number of pages17
DOIs
StatePublished - Aug 14 2008
Event20th International Conference on Scientific and Statistical Database Management, SSDBM 2008 - Hong Kong, China
Duration: Jul 9 2008Jul 11 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5069 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Scientific and Statistical Database Management, SSDBM 2008
CountryChina
CityHong Kong
Period7/9/087/11/08

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