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数据流中一种有效的当前频繁序列挖掘方法 被引量：1

An efficient algorithm for current frequent sequence mining in data stream
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摘要 给出了一种基于滑动窗口挖掘频繁序列算法。该算法给出了ε-近似序列集的定义,利用一种压缩的数据结构GSP-tree来存储和维护整个滑动窗口中各分区的近似序列集,并通过合并各分区的近似序列集来响应用户当前的查询请求。 A sliding window-based algorithm was proposed to mine frequent sequence. The definition of-approximate sequence set was given and a compressed data structure called ＂GSP-tree＂ was introduced to maintain the approximate sequence set of each partition in the whole sliding window.
作者 黄崇争 吴元锡 陈红
机构地区 中国人民大学信息学院数据工程与知识工程教育部重点实验室
出处 《山东大学学报（理学版）》 CAS CSCD 北大核心 2007年第11期37-39,共3页 Journal of Shandong University(Natural Science)
基金 国家自然科学基金资助项目(60673138 60603046) 教育部科学技术研究重点资助项目(106006) 教育部新世纪优秀人才支持计划项目 国家科技攻关课题"国产基础软件平台关键技术及集成技术研究资助项目(2005BA112A02)
关键词 数据流 挖掘 频繁序列 滑动窗口 data stream mining frequent sequence sliding window
分类号 TP399 [自动化与计算机技术—计算机应用技术]
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参考文献3

  • 1MANKU G S, MOTWANI R. Approximate frequency counts over data streams[C]// Proceedings of the 28th International Conference on VLDB, August, 2004, Hong Kong University, Hong Kong. Hong Kong: PPAE, 2004:1123-1134.
  • 2GIANNELLA C, HAN J, PEI J, et al. Mining frequent patterns in data streams at multiple time granularities[J]. Journal of Software, 2003, 29(8) : 192-212.
  • 3CHANG J H, LEE W S. Efficient mining method for retrieving sequential patterns over online data streams[J]. Journal of Information Science, 2006, 31(5) :420-432.

同被引文献29

  • 1缪裕青.频繁闭合项目集的并行挖掘算法研究[J].计算机科学,2004,31(5):166-168. 被引量：5
  • 2Agrawal R, Srikant R. Mining Sequential Patterns. Proe. of the 1 lth Int. Conf. on Data Engineering. Taipei, 1995: 3-14.
  • 3Srikant R, Agrawal R. Mining Sequential Patterns:Gener- alizations and Performance Improvements. Proc. of the 5th Int. Conf. on Extending Database Technology. Avignon, France, 1996:3-17.
  • 4Masseglia F, Cathala F, Poncelet E The PSP Approach for Mining Sequential Patterns. Proc. of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery. Berlin: Springer-Verlag, 1998:174-184.
  • 5Zhang Ming hua, Kao B, Yip Chi Lap, et al. A GSP-based Efficient Algorithm for Mining Frequent Sequences. Proc. of International Conference on Artificial Intelligence. Las Vegas, Nevada, 2001.
  • 6Zaki MJ. SPADE:An Ettieient Algorithm for Mining Frequent Sequences. Machine Learning Journal, 2001,42(1):31-60.
  • 7Han Jia-wei, Pei J, Mortazavi-Asl B, et al. FreeSpan: Fre- quent Pattern-Projected Sequential Pattern Mining. Proc. of 2000 Int. Conf. on Knowledge Discovery and Data Mining. Boston, MA, 2000:355-359.
  • 8Pei J, Han Jia-wei, Mortazavi-Asl B, et al. PrefixSpan:Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Proc. of 2001 Int. Conf. on Data Engineering. Heidelberg, Germany, 2001:215-224.
  • 9Lin Ming-Yen, Lee Suh-Yin. Fast Discovery of Sequential Patterns by Memory Indexing. Proc. of the 4th International Conference of Data Warehousing and Knowledge Discovery. London, Springer-Verlag, 2002:150-160.
  • 10Hsieh Chia-ying, Yang Don-lin, Wu Jung-pin. An Efficient Sequential Pattern Mining Algorithm Based on the 2-Seq- uence Matrix. Proc. of 2008 IEEE International Conference on Data Mining. Pisa, Italy, 2008:583-591.

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山东大学学报（理学版）
2007年 第11期

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