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有效的矩阵加权正负关联规则挖掘算法——MWARM-SRCCCI 预览

MWARM-SRCCCI :efficient algorithm for mining matrix-weighted positive and negative association rules
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摘要 针对现有加权关联规则挖掘算法不能适用于矩阵加权数据的缺陷,给出一种新的矩阵加权项集剪枝策略,构建矩阵加权正负关联模式评价框架SRCCCI,提出一种新的基于SRCCCI评价框架的矩阵加权正负关联规则挖掘算法MWARM—SRCCCI。该算法克服了现有挖掘技术的缺陷,采用新的剪枝技术和模式评价方法,挖掘有效的矩阵加权正负关联规则,避免一些无效和无趣的模式产生。以中文Web测试集CWT200g为实验数据,与现有无加权正负关联规则挖掘算法比较,MWARM—SRCCCI算法的挖掘时间减幅最大可达74.74%。理论分析和实验结果表明,MWARM—SRCCCI算法具有较好的剪枝效果,候选项集数量和挖掘时间明显减少,挖掘效率得到极大提高,其关联模式可为信息检索提供可靠的查询扩展词来源。 In view of the deficiency of the existing weighted association rules mining algorithms which are not applied to deal with matrix-weighted data, a new pruning strategy of itemsets was given and the evaluation framework of matrix-weighted association patterns, SRCCCI ( Support-Relevancy-Correlation Coefficient-Confidence-Interest), was introduced in this paper firstly, and then a novel mining algorithm, MWARM-SRCCCI (Matrix-Weighted Association Rules Mining based on SRCCCI), was proposed, which was used for mining matrix-weighted positive and negative patterns in databases. Using the new pruning technique and the evaluation standard of patterns, the algorithm could overcome the defects of the existing mining techniques, mine valid matrix-weighted positive and negative association rules, avoid the generation of ineffective and uninteresting patterns. Based on Chinese Web test dataset CWT200g (Chinese Web Test collection with 200 GB web Pages) for the experimental data, MWARM-SRCCCI could make the biggest decline of its mining time by up to 74.74% compared with the existing no-weighted positive and negative association rules mining algorithms. The theoretical analysis and experimental results show that, the proposed algorithm has better pruning effect, which can reduce the number of candidate itemsets and mining time and improve mining efficiency markedly, and the association patterns of this algorithm can provide reliable query expansion terms for information retrieval.
作者 周秀梅 黄名选 ZHOU Xiumei, HUANG Mingxuan ( 1. Department of Mathematics and Computer Science, Nanning Prefecture Education College, Nanning Guangxi 530001, China; 2. Office of Scientific Research Administration, Guangxi College of Education, Nanning Guangxi 530023, China)
出处 《计算机应用》 CSCD 北大核心 2014年第10期2820-2826,共7页 journal of Computer Applications
基金 国家自然科学基金资助项目(61262028,61363037) 广西自然科学基金资助项目(2012GxNsFAA053235) 广西教育厅科研项目(201203YB225,2013LX(236) 广西高校优秀人才资助计划项目(桂教人[2011]40号).
关键词 数据挖掘 关联规则 矩阵加权正负关联规则 项集 data mining association rule matrix-weighted positive and negative association rule itemset
作者简介 周秀梅(1972-),女,广西上林人,副教授,主要研究方向:数据挖掘; 黄名选(1966-),男,广西乐业人,教授,CCF会员,主要研究方向:数据挖掘、信息检索。电子邮箱huangmx@mailbox.gxnu.edu.cn
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  • 1AGRAWAL R, IMIELINSKI T, SWAMI A. Mining association rules between sets of items in large database[ C]// Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1993:207 -216. 被引量:1
  • 2宋威,李晋宏,徐章艳,杨炳儒.一种新的频繁项集精简表示方法及其挖掘算法的研究[J].计算机研究与发展,2010(2):277-285. 被引量:16
  • 3钱雪忠,惠亮.关联规则中基于降维的最大频繁模式挖掘算法[J].计算机应用,2011,31(5):1339-1343. 被引量:11
  • 4GLASS D H. Confirmation measures of association rule interesting- ness[J]. Knowledge-Based Systems, 2013,44:65 - 77. 被引量:1
  • 5WU X, ZHANG C, ZHANG S. Efficient mining of both positive and negative association rules [ J]. ACM Transactions on Information Systems, 2004, 22(3) : 381 -405. 被引量:1
  • 6HAMALAINEN W. Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical signif- icance measures [ J]. Knowledge and Information Systems, 2012, 32 (2) :383 -414. 被引量:1
  • 7COKPINAR S, GUNDEM T I. Positive and negative association rule mining on XML data streams in database as a service concept [ J]. Expert Systems with Applications, 2012,39(8) :7503 -7511. 被引量:1
  • 8BHARGAVA R, LADE S. Effective positive negative association rule mining using improved frequent pattern tree [ J]. International Journal of Advanced Research in Computer Science and Software En- gineering, 2013, 3(4) : 193 - 199. 被引量:1
  • 9CAI C H, DA A, FU W C, et al. Mining association rules with weighted items [ C]// Proceedings of IEEE International database Engineering and Application Symposiums. Washington, DC: IEEE Computer Society, 1998:68 -77. 被引量:1
  • 10VO B, COENEN F, LE B. A new method for mining frequent weighted itemsets based on WIT-trees [ J]. Expert Systems with Applications, 2013,40(4) : 1256 - 1264. 被引量:1

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