A database-reduction-based algorithm for episode mining

Yunlan Wang; Xingshe Zhou; Peiqi Liu

doi:10.1109/PDCAT.2006.3

A database-reduction-based algorithm for episode mining

Yunlan Wang, Xingshe Zhou, Peiqi Liu

计算机学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Event Sequence arises naturally in many applications. Episode mining can discovery the knowledge hidden in the event sequence. Currently, the most influential algorithm for episode mining is WINEPI. However, it is likely to suffer from the tendency of generating too many of candidate episodes. In this paper, a novel algorithm named DRE for mining frequent episodes is presented. It studied the conditions for the events which can be pruned from the database, so the size of database is reduced gradually. The performance of algorithm DRE was evaluated and compared with WINEPI algorithm. The results demonstrate that the DRE has better performance.

源语言	英语
主期刊名	Proceedings - Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006
页	123-127
页数	5
DOI	https://doi.org/10.1109/PDCAT.2006.3
出版状态	已出版 - 2006
活动	7th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006 - Taipei, 中国台湾期限: 4 12月 2006 → 7 12月 2006

出版系列

姓名	Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings

会议

会议	7th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006
国家/地区	中国台湾
市	Taipei
时期	4/12/06 → 7/12/06

访问文件

10.1109/PDCAT.2006.3

其它文件与链接

链接到 Scopus 的出版物

引用此

Wang, Y., Zhou, X., & Liu, P. (2006). A database-reduction-based algorithm for episode mining. 在 Proceedings - Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006 (页码 123-127). 文章 4032163 (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings). https://doi.org/10.1109/PDCAT.2006.3

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title = "A database-reduction-based algorithm for episode mining",

abstract = "Event Sequence arises naturally in many applications. Episode mining can discovery the knowledge hidden in the event sequence. Currently, the most influential algorithm for episode mining is WINEPI. However, it is likely to suffer from the tendency of generating too many of candidate episodes. In this paper, a novel algorithm named DRE for mining frequent episodes is presented. It studied the conditions for the events which can be pruned from the database, so the size of database is reduced gradually. The performance of algorithm DRE was evaluated and compared with WINEPI algorithm. The results demonstrate that the DRE has better performance.",

author = "Yunlan Wang and Xingshe Zhou and Peiqi Liu",

year = "2006",

doi = "10.1109/PDCAT.2006.3",

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Wang, Y, Zhou, X & Liu, P 2006, A database-reduction-based algorithm for episode mining. 在 Proceedings - Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006., 4032163, Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, 页码 123-127, 7th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006, Taipei, 中国台湾, 4/12/06. https://doi.org/10.1109/PDCAT.2006.3

A database-reduction-based algorithm for episode mining. / Wang, Yunlan; Zhou, Xingshe; Liu, Peiqi.
Proceedings - Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006. 2006. 页码 123-127 4032163 (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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