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一种启发式XML结构重构算法

Algorithm of refactoring XML structure with heuristic strategy
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摘要 基于海量XML文档查询时信息关联和服务请求多样性的需求,提出一个重构XML结构的频繁向量选择增量模式树(XFP-tree)算法。该算法以XML键为基础,利用向量矩阵处理方法、投影频繁模式树实现XML结构的分裂、合并、更改与取消等操作,同时讨论XML键向量矩阵频繁项集的划分规则及相应启发式策略与支持度阈值。对比其他关联算法,一系列仿真实验表明所提出算法具有一定的有效性及合理性,是重构XML结构的一种有效尝试。 Considering the demand of the date relationship and service request multiform based on XML documents, this paper proposed a new frequent pattern tree algorithm for selected incremental vector items set of refactoring XML structure (XFP-tree). Bases on the XML Key, the algorithm firstly dealt with XML structure to vector matrix, then used project frequent pattern tree to optimize the XML structure through dissociating, uniting, updating and canceling to satisfy the conciseness of the XML structure and query multiversity. Combining project and tree-structure manipulation, this paper discussed the dividing rule of xml key vector matrix frequent pattern. This rule improved the algorithm efficiency by establishing heuristic strategy and support thresholds. Contrasted with other algorithms of Association Rule, a series of emulation experiments show that this method has the effectiveness and feasibility as an efficacious attempt of refactoring XML structure.
出处 《计算机应用》 CSCD 北大核心 2008年第7期1696-1699,共4页 journal of Computer Applications
基金 湖南信息职业学院科技创新项目(108652006011) 湖南省教育厅科研基金资助项目(05c671)
关键词 XML结构重构 XML键 向量矩阵 投影频繁模式树 XML structure refactoring XML key vector matrix project frequent pattern tree
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参考文献8

  • 1HOFFMANN B, JANSSENS D, EETVELDE N V. Cloning and expanding graph transformation rules for refactoring[ J]. Electronic Notes in Theoretical Computer Science, 2006, 152(12) : 53 - 67.
  • 2AMBLERSW,SADLAGE P J.数据库重构[M].王海鹏,译.北京:机械工业出版社,2007:1-153.
  • 3GRAHNE G, ZHU JIAN-FEI. Fast algorithms for frequent itemset mining using FP-trees[ J], IEEE Transactions on Knowledge and Data Engineering, 2005, 17(10) : 1347 - 1365.
  • 4AGARWAL R C, AGGARWAL C C, PRASAD V V. A tree projection algorithm for generation of frequent item sets [ J]. Journal of Parallel and Distributed Computing, 2001,61 (3) : 350 - 371.
  • 5颜跃进,李舟军,陈火旺.基于FP-Tree有效挖掘最大频繁项集[J].软件学报,2005,16(2):215-222. 被引量:68
  • 6LEE J T, WANG CHUN-SHENG. An efficient algorithm for mining frequent inter-transaction patterns [ J]. Information Sciences, 2007, 177(17) : 3453 -3476.
  • 7CHEN Z S, HSU S C. Mining frequent tree-like patterns in large datasets[ J]. Data & Knowledge Engineering, 2007, 62(1) : 65 -83.
  • 8Sigmod Record[ EB/OL]. [ 2007 - 06 - 01 ]. http://www. sigmod. org/record/xml/index. xml.

二级参考文献13

  • 1宋余庆 朱玉全 孙志辉 陈耿.基于FP—Tree的最大频繁项集挖掘及其更新算法.软件学报,2003,14(9):1586—1592[J].http://wwwjos.org.cn/1000-9825/14/1586.htm,:.
  • 2Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proc. of the 20th Int'l Conf. on VLDB. 1994. 487-499.http://www.almaden.ibm.conVcs/people/srikant/papers/vldb94.pdf.
  • 3Bayardo R. Efficiently mining long patterns from databases. In: Haas LM, ed. Proc. of the ACM SIGMOD Int'l Conf. on Management of Data. New York: ACM Press, 1998. 85-93.
  • 4Burdick D, Calimlim M, Gehrke J. Mafia: A maximal frequent itemset algorithm for transactional databases. In: Proc. of the 17th Int'l Conf. on Data Engineering. 2001. 443-452. http://www.cs.cornell.edu/boom/2001 sp/yiu/mafia-camera.pdf.
  • 5Gouda K, Zaki MJ. Efficiently mining maximal frequent itemsets. In: Proc. of the 1st IEEE Int'l Conf. on Data Mining. 2001.163-170. http ://www.cs .tau. ac .il/-fiat/dmsem03/E fficient%20Mining%20Maxmal%20Frequent%20Itemsets%20-%202001 .pdf.
  • 6Wang H, Li QH. An improved maximal frequent itemset algorithm. In: Wang GY, eds. Proc of the Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, the 9th Int'l Conf (RSFDGrC 2003). LNCS 2639, Heidelberg: Springer-Verlag, 2003. 484-490.
  • 7Zhou QH, Wesley C, Lu BJ. SmartMiner: A depth 1st algorithm guided by tail information for mining maximal frequent itemsets.In: Proc of the IEEE Int'l Conf on Data Mining (ICDM2002). 2002. 570-577. http://www.serviceware.com/pdffiles/datasheets/ServiceWare-Smartminer-Datasheet.pdf.
  • 8Grahne G, Zhu JF. High performance mining of maximal frequent itemsets. In: Proc of the 6th SIAM Int'l Workshop on High Performance Data Mining (HPDM 2003). 2003. 135-143. http://www.cs.concordia.ca/db/dbdm/hpdm03.pdf.
  • 9Agarwal RC, Aggarwal CC, Prasad VVV. Depth 1 st generation of long patterns. In: Proc. of the 6th ACM SIGKDD Int'l Conf on Knowledge Discovery and Data Mining. 2000. 108-118. http://www.cs.tau.ac.il/-fiat/dmsem03/Depth%20First%20Generation%20of%20Long%20Patterns%20-%202000.pdf.
  • 10Wang H, Xiao ZJ, Zhang H J, Jiang SY. Parallel algorithm for mining maximal frequent patterns. In: Zhou XM, ed. Advanced Parallel Processing Technologies (APPT 2003). LNCS 2834, Heidelberg: Springer-Verlag, 2003. 241-248.

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