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事件序列上的频繁情节挖掘算法

Algorithms for Mining Frequent Episodes on the Event Sequences
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摘要 事件序列上的频繁情节挖掘是时序数据挖掘领域的热点之一,基于非重叠发生的支持度定义,提出一个频繁情节挖掘算法NONEPI++,该算法首先通过情节串接产生候选情节,然后通过预剪枝和计算情节发生的时间戳来产生频繁情节.算法只需扫描事件序列一次,大大提高了情节挖掘的效率.实验证明,NONEPI++算法能有效地挖掘频繁情节. Mining frequent episodes on the event sequences is one of the hot areas of data mining. In this paper, support based on non-overlapped occurrence is definited. We propose an algorithm called NONEPI++ for mining frequent episodes, which firstly generate candidate episodes by join episodes, then generate frequent episodes by pre-pruning and timestamp calculating. The algorithm can improve the efficiency of mining episodes. Experiments show that NONEPI++algorithm can effectively mine frequent episodes.
出处 《计算机系统应用》 2014年第12期202-205,共4页 Computer Systems & Applications
关键词 事件序列 频繁情节 非重叠发生 event sequence frequent episode non-overlapped occurrence
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参考文献8

  • 1Manilla H, Toivonen H, Verkamo A. Discovering frequent episodes in sequences. Proc. of the First International Conference on Knowledge Discovery and Data Mining. 1995 210-215.
  • 2Mannila H, Toivonen H, Verkamo AI. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1997, 1(3): 259-289.
  • 3Mannila H, Toivonen H. Discovering generalized episodes using minimal occurrences. KDD, 1996, 96: 146-151.
  • 4Laxman S, Sastry PS, Unnikrishnan KP. Discovering frequent episodes and learning hidden markov models: A formal connection. IEEE Trans. on Knowledge and Data Engineering, 2005, 17(11): 1505-1517.
  • 5朱辉生,汪卫,施伯乐.基于频繁闭情节及其生成子的无冗余情节规则抽取[J].计算机学报,2012,35(1):53-64. 被引量:7
  • 6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. 被引量:6
  • 7Zhu H, Wang P, He X, et al. Efficient episode mining with minimal and non-overlapping occurrences. 2010 IEEE 10th International Conference on Data Mining (ICDM). 2010. 1211-1216.
  • 8林树宽,乔建忠.一种基于情节矩阵和频繁情节树的情节挖掘方法[J].控制与决策,2013,28(3):339-344. 被引量:3

二级参考文献42

  • 1赵峰,李庆华,金莉.一种主动容错的序列流并行分析算法[J].软件学报,2006,17(12):2416-2424. 被引量:2
  • 2潘定,沈钧毅.时态数据挖掘的相似性发现技术[J].软件学报,2007,18(2):246-258. 被引量:41
  • 3Mannila H, Toivonen H, Verkamo A I. Discovering fre- quent episodes in sequences//Proceedings of the 1st ACM SIGKDD Conference on Knowledge Discovery and Data Min- ing. Montreal, Canada, 1995:210-215.
  • 4Hatonen K, Klemettinen M, Mannila H, Ronkainen P, Toivonen H. Knowledge discovery from telecommunication network alarm databases//Proceedings of the 12th IEEE In- ternational Conference on Data Engineering. New Orleans, Louisiana, 1996: 115-122.
  • 5Meger N, Rigotti C. Constraint based mining of episode rules and optimal window sizes//Proceedings of the 8th Eu- ropean Conference on Principles and Practice of Knowledge Discovery in Databases. Pisa, Italy, 2004:313-324.
  • 6Patnaik D, Marwah M, Sharma R, Ramakrishnan N. Sus- tainable operation and management of data center chillers using temporal data mining//Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Paris, France, 2009:1305-1313.
  • 7Hwang K, Cai M, Chen Y, Qin M. Hybrid intrusion detec- tion with weighted signature generation over anomalous in- ternet episodes. IEEE Transactions on Dependable and Secure Computing, 2007, 4(1): 41-55.
  • 8Ng A, Fu A. Mining frequent episodes for relating financial events and stock trends//Proceedings of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Seoul, Korea, 2003:27-39.
  • 9Lo D, Khoo S, Liu C. Efficient mining of recurrent rules from a sequence database//Proceedings of the 13th Interna- tional Conference on Database Systems for Advanced Appli- cations. New Delhi, India, 2008:67-83.
  • 10Wang P, Wang H, Liu M, Wang W. An algorithmic approach to event summarization//Proceedings of the ACM SIGMOD International Conferenee on Management of Data. Indianapolis, Indiana, USA, 2010:183-194.

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