PROWL: An efficient frequent continuity mining algorithm on event sequences

Kuo Yu Huang, Chia Hui Chang, Kuo Zui Lin

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

12 Scopus citations

Abstract

Mining association rule in event sequences is an important data mining problem with many applications. Most of previous studies on association rules are on mining intra-transaction association, which consider only relationship among the item in the same transaction. However, intra-transaction association rules are not a suitable for trend prediction. Therefore, inter-transaction association is introduced, which consider the relationship among itemset of multiple time instants. In this paper, we present PROWL, an efficient algorithm for mining inter-transaction rules. By using projected window method and depth first enumeration approach, we can discover all frequent patterns quickly. Finally, an extensive experimental evaluation on a number of real and synthetic database shows that PROWL significantly outperforms previous method.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsYahiko Kambayashi, Mukesh Mohania, Wolfram Wöß
PublisherSpringer Verlag
Pages351-360
Number of pages10
ISBN (Print)354022937X, 9783540229377
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3181
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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