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关联规则挖掘技术在导读系统中的应用

Association Rules Mining Technology Used in Guide Reading System
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摘要 论文提出了一种步长为2的宽度优先层次搜索的关联规则挖掘算法。该算法根据素数只有两个约数的性质,成功地把高维二进制事务库转化为一维事务库,把集合的运算转化为求约数的数值运算,把统计数据项频次的问题转化为统计事务的公约数。规则判断条件采用的是抽样平均可信度。同时,探讨了时间对关联规则挖掘产生的影响以及在导读系统中的应用。在仿真实验中,效果比较理想。 This paper puts forward an association rule mining algorithm of preferential level searching in the width of step 2 acording to the truth that prime number has only two divisors,the algorithm sucessfully changes high-dimension binary system routine assemble into one-dimension routine system,changes assemble operation,into numeral value operation of seeking divisors,changes counting data frequency into counting common divisors of routine sampling average credibility is employed in routine judging conditions.Meanwhile this paper discusses the influence of time on associations rule mining and the application in guide reading system.The experiment show the effects are more ideal.
作者 陈爽 赵连朋 姚明海 孙浩
机构地区 渤海大学
出处 《计算机工程与应用》 CSCD 北大核心 2006年第33期191-194,共4页 Computer Engineering and Applications
关键词 关联规则 频集 素数存储 平均可信度 知识发现 Association rales freguency itemset prime namber storage average credibility knowledge discovery
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献5

  • 1AGRAWAL R,IMIELINSKI T,SWAMI A.Mining association rules between sets of items in large database[C]//BUNEMAN P,JAJODIAS.Proc of 1993 ACM SIGMOD Conf on Management of Data.Washington DC:ACM Press,1993:207-216
  • 2刘乃丽,李玉忱,马磊.一种有效且无冗余的快速关联规则挖掘算法[J].计算机应用,2005,25(6):1396-1397. 被引量：7
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二级参考文献12

  • 1AGRAWAL R, IMIELINSKI T, SWAMI A. Mining association rules between sets of items in large databases[ A]. In: Buneman P, Jajodia S, eds. Proc. of the ACM SIGMOD Conf. on Management of Data (SIGMOD'93) [ C] . New York: ACM Press, 1993. 207-216.
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  • 6PASQUIER N, BASTIDE Y, TAOUIL R, et al. Discovering frequent closed itemsets for association rules[ Z]. In 7th Intl Conf on Database Theory, Jan 1999.
  • 7PASQUIER N, BASTIDE Y, TAOUIL R, et al. Efficient mining of association rules using closed itemset lattices[ J]. Information Systems, 24(1):25-46, 1999.
  • 8ZAKI MJ, HSIAO C - J. CHARM: An efficient algorithm for closed association rule mining[ A]. Technical Report 99 - 10, Computer Science Dept, Rensselaer Polytechnic Institute[ C]. October 1999.
  • 9ZAKI MJ. Generating non-redundant association rules[ A]. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining[ C]. Boston, Massachusetts, United States, 2000.34 - 43.
  • 10BASTIDE Y, PASQUIER N, TAOUIL R, et al. Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets [A]. Proceedings of the First International Conference on Computational Logic[ C], 2000.972 - 986.

共引文献6

计算机工程与应用
2006年 第33期

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