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使用有向图挖掘时间间隔序列模式 被引量：2

Using digraph to discover time-interval sequential patterns
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摘要 在Chen等人提出的时间间隔序列模式概念的基础上,给出了一种利用有向图搜索时间间隔序列模式的算法。实验表明所提出的算法较I-Apriori算法更加快速和高效。 Based on the concept of time interval sequence pattern which was introduced by Chen, Jiang and Ko, an algorithm of using digraph to discover time interval sequence patterns is proposed. Experimental results show that the algorithm is faster than I-Apriori.
作者 刘俊侠
机构地区 辽宁科技大学计算机科学与工程学院
出处 《计算机科学与探索》 CSCD 2008年第6期666-672,共7页 Journal of Frontiers of Computer Science and Technology
关键词 序列模式 序列数据 时间间隔 sequence patterns sequence data time interval
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献2

  • 1Mohammed J. Zaki. SPADE: An Efficient Algorithm for Mining Frequent Sequences[J] 2001,Machine Learning(1-2):31～60
  • 2Heikki Mannila,Hannu Toivonen,A. Inkeri Verkamo. Discovery of Frequent Episodes in Event Sequences[J] 1997,Data Mining and Knowledge Discovery(3):259～289

同被引文献24

  • 1Han Jiawei, Pei Jian, Yin Yiwen. Mining frequent pattern without candidate generation [C] //ACM SIGMOD international conference, Dallas, TX, USA, 2000.
  • 2Cheung D W, Han J W, Ng V T, et al.. Maintenance of discovered association rules in large database: an incremental updating technique [C] //Proceedings of the Twelfth International Conference on Data Engineering,New Orleans, Louisiana, USA, 1996:106-114.
  • 3Hong Tzung-Pei, Lin Chun-Wei, Wu Yu-Lung. Incrementally fast updated frequent pattern trees [J]. Expert Systems with Applications, 2008,34 (4): 2424-2435.
  • 4Liu Hongyan, Wang Xiaoyu, He Jun, et al.. Top-down mining of frequent closed patterns from very high dimensional data [J]. Information Sciences, 2009,179 (7): 899-924.
  • 5Tan Jun, Bu Yingyong, Yang Bu. An efficient close frequent pattern mining algorithm [C] //Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation, 2009: 528-531.
  • 6Nanopoulos A, Manolopoulos Y. Mining pattems from graph traversals [J]. Data and Knowledge Engineering, 2001,37 (3): 243-266.
  • 7Geng Runian, Dong Xiang, jun. WTMaxMiner: efficient mining of maximal frequent patterns based on weighted directed graph traversals [C] //IEEE International Conference on Cybernetics and Intelligent Systems, 2008: 1081-1086.
  • 8Suominen O,Mader C.Assessing and improving the quality of SKOS vocabularies[J].Journal on Data Semantics,2014,3(1):47-73.
  • 9Carlo B,Daniele B,Federico C,et al.A data quality methodology for heterogeneous data[J].International Journal of Database Management Systems,2011,3(1):60-79.
  • 10Batini C,Cappiello C,Francalanci C,et al.Methodologies for data quality assessment and improvement[J].ACM Computing Surveys,2009,41(3):75-79.

引证文献2

二级引证文献6

计算机科学与探索
2008年 第6期

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