Mining periodic patterns in sequence data

Kuo Yu Huang, Chia Hui Chang

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

16 Scopus citations

Abstract

Periodic pattern mining is the problem that regards temporal regularity. There are many emerging applications in periodic pattern mining, including web usage recommendation, weather prediction, computer networks and biological data. In this paper, we propose a Progressive Timelist-Based Verification (PTV) method to the mining of periodic patterns from a sequence of event sets. The parameter min_rep, is employed to specify the minimum number of repetitions required for a valid segment of non-disrupted pattern occurrences. We also describe a partitioning approach to handle extra large/long data sequence. The experiments demonstrate good performance and scalability with large frequent patterns.

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
Pages401-410
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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