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一种更新关联规则的方法 被引量:6

Method of Updated Association Rules
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摘要 数据挖掘中IUA算法存在遗漏频繁项目集致使有的关联规则挖掘不出来的问题,在分析Apriori算法、IUA算法等经典关联规则挖掘算法的基础上,提出了一种基于最近挖掘结果的更新算法HIUA。HIUA算法吸收了Apriori算法和IUA算法的优点,在改变最小支持度和基于最近挖掘结果的条件下,从生成尽可能少的候选项目集考虑,从而得到完整的新频繁项目集,提高了算法的效率。 In data mining, IUA algorithm has a problem that the frequent itemsets are not minined completely. Apriori algorithm in the analysis, such as classical correlation rules IUA algorithms excavation algorithms based on the results presentes an update on recent excavations algorithm called HIUA. HIUA algorithms absorbed Apriori algorithm and the advantages IUA algorithms, the smallest change in the support for and based on the results of recent excavations conditions from the pending option sets generated minimal consideration given complete sets of innovative procedures, thereby enhancing the efficiency of algorithms.
作者 张宗平
出处 《计算机工程》 CAS CSCD 北大核心 2008年第1期64-65,68,共3页 Computer Engineering
关键词 关联规则 更新算法 数据挖掘 频繁项目集 association rules updating algorithm data mining frequent item set
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  • 1[1]AGRAWAL R, IMIELINSKI T, SWAMI A. Mining association rules between sets of items in large database[A].Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data[C]. Washington: ACM,1993. 207-216.
  • 2[2]ZHONG N, YAO Y, OHSUGA S. Peculiarity oriented multi-database mining[A]. In.. ZYTKOW J, RAUCH J.Principles of Data Mining and Knowledge Discovery[M]. Berlin: Springer-Verlag, 1999. 136-146.
  • 3[3]NIECHUYS S, WOLF R. Foundations of Inductive Logic Programming[M]. Berlin: Springer, 1997. 163-177.
  • 4[4]FAYYAD U. Knowledge discovery in databases: An overview[A]. In: DZEROSKI S, LAVRAC N. Relational Data Mining[C]. Berlin: Springer, 2001. 29-47.
  • 5[5]QUINLAN J. Learning logical definitions from relations[J]. Machine Learning, 1990, 5: 239-266.
  • 6[6]ZHONG N, YAO Y, OHSHIMA M, et al. Interestingness, peculiarity, and multi-database mining[A]. Proc 2001IEEE Intemational Conference on Data Mining (IEEE ICDM01)[C]. Washington: IEEE Computer Society, 2001.566-573.
  • 7[7]LIU H, SETIONO R. Feature selection via discretization of numeric attributes[J]. IEEE Trans Knowledge and Data Eng, 1997, 9(4): 642-645.
  • 8[8]LIN T. Granular computing on binary relations 1: Data mining and neighborhood systems[A]. In: POLKOWSKI L,SKOWRON A. Rough Sets in Knowledge Discovery 1, in Studies in Fuzziness and Soft Computing Series[C].Heidelberg: Physica-Verlag, 1998. 107-121.
  • 9程继华,郭建生,施鹏飞.挖掘所关注规则的多策略方法研究[J].计算机学报,2000,23(1):47-51. 被引量:22

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