Mining periodic patterns in sequence data

Kuo Yu Huang, Chia Hui Chang

研究成果: 書貢獻/報告類型篇章同行評審

17 引文 斯高帕斯(Scopus)

摘要

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.

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主出版物標題Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
編輯Yahiko Kambayashi, Mukesh Mohania, Wolfram Wöß
發行者Springer Verlag
頁面401-410
頁數10
ISBN(列印)354022937X, 9783540229377
DOIs
出版狀態已出版 - 2004

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3181
ISSN(列印)0302-9743
ISSN(電子)1611-3349

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