PROWL: An efficient frequent continuity mining algorithm on event sequences

Kuo Yu Huang, Chia Hui Chang, Kuo Zui Lin

研究成果: 書貢獻/報告類型篇章同行評審

11 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
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351-360
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

指紋

深入研究「PROWL: An efficient frequent continuity mining algorithm on event sequences」主題。共同形成了獨特的指紋。

引用此