[1]袁红娟.BIDEFCE:一种基于双向扩展的频繁闭情节挖掘算法[J].南京师范大学学报(工程技术版),2013,13(04):051.
 Yuan Hongjuan.BIDEFCE:An Algorithm for Mining Frequently Closed Episodes Based on Bidirectional Extension[J].Journal of Nanjing Normal University(Engineering and Technology),2013,13(04):051.
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BIDEFCE:一种基于双向扩展的频繁闭情节挖掘算法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
13卷
期数:
2013年04期
页码:
051
栏目:
出版日期:
2013-12-31

文章信息/Info

Title:
BIDEFCE:An Algorithm for Mining Frequently Closed Episodes Based on Bidirectional Extension
作者:
袁红娟
泰州学院数理信息学院,江苏 泰州 225300
Author(s):
Yuan Hongjuan
School of Mathmatics and Information Technology,Taizhou University,Taizhou 225300,China
关键词:
非重叠最小发生闭情节双向扩展深度优先
Keywords:
non-overlappingminimal occurrencesclosed episodesbidirectional extensiondepth first
分类号:
TP311
文献标志码:
A
摘要:
在事件序列上挖掘频繁闭情节时,为避免维护频繁情节集,加快挖掘进度,提出基于双向扩展的频繁闭情节挖掘算法BIDEFCE.该算法基于非重叠的最小发生的支持度定义和深度优先搜索策略,在生成新频繁情节的同时,采用向前和向后扩展检查,尽早判断并淘汰非闭情节,将待定情节加入频繁闭情节超集FCE中.然后再对FCE中的情节进行闭合性检查,保留真正的闭情节.该算法避免维护频繁情节集,只需维护频繁闭情节超集,节省存储空间,提高运行效率.实验证实BIDEFCE算法在事件序列上能有效挖掘频繁闭情节.
Abstract:
In order to avoid maintaining frequent episodes set in event sequences while mining closed frequent episodes and to speed up the progress of mining,this paper puts forward the algorithm BIDEFCE for mining frequent closed episodes based on bidirectional extension.Algorithm BIDEFCE discovers all frequently closed episodes by employing the supportive definition of non-overlapping minimal occurrences and the depth-first search strategy.BIDEFCE uses the forward and backward extension check in the generation of frequent episodes,in order to judge and eliminate non-closed episodes as soon as possible,the other episodes are added into frequently closed episodes superset FCE.The true closed episodes will be reserved after the closure check.Moreover,BIDEFCE saves store space,improves operation efficiency,and avoids maintaining the frequent episodes set at the same time.Experiments have proved that BIDEFCE can effectively mine closed frequent episodes in event sequences.

参考文献/References:

[1] Julisch K,Dacier M.Mining intrusion detection alarms for actionable knowledge[C]//Proc of the 8th ACM SIGKDD Int'l Conf.on Knowledge Discovery in Data Mining.New York:ACM Press,2002:366-375.
[2]Cortes C,Fisher K,Pregibon D,et al.Hancock:A language for extracting signatures from data streams[C]//Proc of the 6th ACM SIGKDD Int'l Conf.on Knowledge Discovery in Data Mining.New York:ACM Press,2000:9-17.
[3]Ng A,Fu AW.Mining frequent episodes for relating financial events and stock trends[C]//Proceedings of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining.Seoul,2003:27-39.
[4]Mannila H,Toivonen H,Verkamo A I.Discovering frequent episodes in sequences[C]//Proceedings of the 1st ACM SICKDD Conference on Knowledge Discovery and Data Mining.Montreal,1995:210-215.
[5]Zhou W,Liu H,Cheng H.Mining closed episodes from event sequences efficiently[C]//The Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD).India.2010:310-318.
[6]Tatti N,Cule B.Mining closed strict episodes[C].Sydney:IEEE International Conference on Data Mining.2010:501-510.
[7]Zhu H,Wang P,Wang W,et al.Discovering frequent closed episodes from an event sequence[C].Brisbane:WCCI 2012 IEEE World Congress on Computational Intelligence.2012:2 292-2 299.
[8]Zhu H,Wang P,He X,et al.Efficient episode mining with minimal and non-overlapping occurrences[C]//Proceedings of the 10th IEEE International Conference on Data Mining.Sydney,2010:1211-1216.
[9]朱辉生,汪卫,施伯乐.基于最小且非重叠发生的频繁闭情节挖掘[J].计算机研究与发展,2013,50(4):852-860.
Zhu Huisheng,Wang Wei,Shi Baile.Frequent closed episode mining based on minimal and non-overlapping occurrences[J].Journal of Computer Research and Development,2013,50(4):852-860.(in Chinese)
[10]Wang J,Han J.BIDE:efficient mining of frequent closed sequences[C]//Proceedings of the 20th International Conference on Data Engineering.Boston:IEEE,2004:79-90.

备注/Memo

备注/Memo:
收稿日期:2013-05-04.
通讯联系人:袁红娟,讲师,研究方向:数据挖掘,E-mail:yhj_blue@126.com
更新日期/Last Update: 2013-12-30