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FP-Growth关联规则挖掘的改进算法 被引量：2

An improved FP-Growth algorithm of association rules
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摘要 文章通过对FP-Growth算法分析,提出的改进算法能有效地减少需遍历的树的节点数,从而降低了时间开销。实验表明:改进算法能明显地提高挖掘效率。 Through analyzing the FP- Growth algorithm, the author proposed an improved algorithm, whieh can decrease the number of traversal tree node, to reduce the cost of time. The experiment results showed that the improved algorithm can improve mining efficiency cleady.
作者 张星 李蓓
机构地区 平顶山工学院计算机科学与工程系
出处 《平顶山工学院学报》 2008年第1期21-24,共4页 Journal of Pingdingshan Institute of Technology
关键词 关联规则 FP—Growth算法 FP—Tree 数据挖掘 associstion rules frequent pattern growth algorithm FP- Tree data mining
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7

  • 1[美]Mehmed Kantardzic著,闪四清,陈茵,程雁译.数据挖掘概念、模型、方法和算法[M].北京:清华大学出版社,2003.
  • 2[加]Jiawei Han Michelime Kamber著,范明,孟小峰译.数据挖掘概念与技术[M].北京:机械工业出版社,2001.
  • 3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
  • 4何炎祥,向剑文,朱骁峰,孔维强.不产生候选的快速投影频繁模式树挖掘算法[J].计算机科学,2002,29(11):71-75. 被引量：11
  • 5Balazs Racz. An FP-Growth Variation without Rebuilding the FP-Tree[J]. Budapest: Computer and Automation Research Institute of the hungarian Academy of Sciences,IEEE ICDM'04 Workshop FIMI'04,2004.
  • 6Jiawei Han.Data mining-current status and research directions[M] .Seminar Presentation,2001(2):197 - 243.
  • 7Han,Jiawei,Pei,Jian,Yin,Yiwen, Mao,Runying. Mining Frequent Parterre without Candidate Generation[M], Data Mining and Discovery, 2004,8(1)53-87.

二级参考文献27

  • 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.

共引文献75

同被引文献33

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二级引证文献4

平顶山工学院学报
2008年 第1期

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