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SCTree:一种互异情节模式挖掘算法 被引量：1

SCTREE:A MINING ALGORITHM FOR DISTINCT EPISODES
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摘要 现有的事件序列情节模式挖掘算法多是基于滑动窗口和非重叠出现的。目前没有有效算法挖掘基于互异出现的互异情节模式。为此,提出带状态计数的前缀树结构(SCTree)来生成互异情节模式候选集,进行互异计数和裁剪。为减少数据库扫描次数,提出SCTree的主动扩展技术。实验表明了算法的有效性和高效性。 Most of existing mining algorithms for event sequence episodes are sliding window-based and non-overlapped occurrences based.There is no efficient algorithm to mine the distinct episodes that is distinct occurrences-based.This paper introduces a novel state-counted prefix-tree（SCTree） for generating candidate set of distinct episodes and to make distinction counting and pruning.In order to reduce the scanning of database,an eager extension technology of SCTree is proposed.Experimental results show the efficiency and effectiveness of the proposed algorithm.
作者 黄鹏 王鹏 汪卫
机构地区 复旦大学计算机科学技术学院
出处 《计算机应用与软件》 CSCD 北大核心 2013年第3期177-181,共5页 Computer Applications and Software
关键词 数据挖掘 频繁情节模式 互异出现 带状态计数的前缀树 Data mining Frequent episodes Distinct occurrences State-counted prefix-tree
分类号 TP301 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献9

  • 1Agrawal R, Srikant R. Mining Sequential Patterns [ C ]//Proc. of Intl. Conf. on Data Engineering, Taipei, Taiwan, 1995:3 - 14.
  • 2Seno M, Kartpis G. SLPMiner:An Algorithm for Finding Frequent Se- quential Patterns Using Length-Decreasing Support Constraint [ C ]// Proc. of the 14th Intl. Conf. on Data Engineering, Maebashi City, Ja- pan, December,2002 : 418 - 425.
  • 3Mannila H, Toivonen H, Verkamo A I. Discovery of Frequent Episodes in Event Sequences [ J]. Data Mining and Knowledge Discovery, 1997, 1 (3) :259 - 289.
  • 4Casas-Garrlga G. Discovering unbounded episodes in sequential data [ C]//Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases ( PKDD' 03 ), Cavtat- Dubvrovnik, Croatia,2003:83 - 94.
  • 5Meger N ,Rigotti C. Constraint-based mining of episode rules and opti- mal window sizes [ C ]//Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases ( PKDD' 04 ), Pisa, Italy, 2004.
  • 6Joshi M V, Karypis G, Kumar V. A Universal Formulation of Sequential Patterns[ C ]//Proc, of the KDD' 2001 workshop on Temporal Data Mining, San Francisco, CA, August,2001.
  • 7Laxman S, Sastry P S, Unnikrishnan K P. Discovering frequent episodes and learning Hidden Markov Models : A formal connection [ J ]. IEEE Transactions on Knowledge and Data Engineering,2005,17 (11 ) :1505 -1517.
  • 8Laxman S, Sastry P S, Unnikrishnan K P. A Fast Algorithm For Finding Frequent Episodes In Event Streams [ C ]//Proc. of the KDD' 2007, San Jose, California, USA. August 12 - 15,2007.
  • 9黄鹏,王鹏,汪卫.面向事件流的频繁片断计数算法[J].计算机科学与探索,2010,4(10):909-917. 被引量：1

二级参考文献8

  • 1Agrawal R,Srikant R.Mining sequential patterns[C] //Proc of Intl Conf on Data Engineering,1995:3-14.
  • 2Seno M,Kartpis G.SLPMiner:An algorithm for finding frequent sequential patterns using length-decreasing support constraint[C] //Proc of the 14th Infl Conf on Data Engineering,Maebashi City,Japan,December,2002:418-425.
  • 3Mannila H,Toivonen H,Verkamo A I.Discovery of frequent episodes in event sequences[J].Data Mining and Knowledge Discovery,1997,1(3):259-289.
  • 4Casas-Garriga G.Discovering unbounded episodes in sequential data[C] //Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'03),Cavtat-Dubvrovnik,Croatia,2003:83-94.
  • 5Meger N,Rigotti C.Constraint-based mining of episode rules and optimal window sizes[C] //Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'04),Pisa,Italy,Sept 2004.
  • 6Joshi M V,Karypis G,Kumar V.A universal formulation of sequential patterns[C] //Proc of the KDD'2001 Workshop on Temporal Data Mining,San Francisco,CA,August 2001.
  • 7Laxman S,Sastry P S,Unnikrishnan K P.Discovering frequent episodes and learning hidden Markov models:A formal connection[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(11):1505-1517.
  • 8Laxman S,Sastry P S,Unnikrishnan K P.A fast algorithm for finding frequent episodes in event streams[C] //Proc of the KDD' 2007,San Jose,California,USA,August 12-15,2007.

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计算机应用与软件
2013年 第3期

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