关联规则挖掘综述.doc
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关联规则挖掘综述,页数 9字数 7791 摘要本文介绍了关联规则挖掘的研究情况，提出了关联规则的分类方法，对一些典型算法进行了分析和评价，指出传统关联规则衡量标准的不足，归纳出关联规则的价值衡量方法，展望了关联规则挖掘的未来研究方向。关键词数据挖掘,关联规则,频集,olapabstractthis paper provi...
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关联规则挖掘综述
页数 9 字数 7791
摘要 本文介绍了关联规则挖掘的研究情况，提出了关联规则的分类方法，对一些典型算法进行了分析和评价，指出传统关联规则衡量标准的不足，归纳出关联规则的价值衡量方法，展望了关联规则挖掘的未来研究方向。
关键词 数据挖掘,关联规则,频集,OLAP
Abstract This paper provides a survey of the study in association rule generation,brings forward a classification of association rule,reviews and analyses some typical algorithms,points out the weakness of the traditional measure method,concludes the measure method of the association rule’s value,views some future directions in association rule generation.
Key Words Data Mining, Association Rules, Large Itemset,OLAP
参考文献
1 R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD Conference on Management of data, pp. 207-216, 1993.
2 Aly HH,Taha Y, Amr AA. Fast mining of association rules in large-scale problems.In:Abdel-Wahab H,Jeffay K,eds.Proc.of the 6th IEEE Symp.on Computers and
Communications(ISCC 2001).New York:IEEE Computer Society Press,2001.107-113.
3 Tsai CF,Lin YC,Chen CP.A new fast algorithms for mining association rules in large databases.In:Kamel AE,Mellouli K,Borne.P,eds.Proc.of the 2002 IEEE Int’1 Confon Systems, Man and Cybernetics(SMC 2002).IEEE Computer Soceity Press,2002.251-256.
4 S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic Itemset counting and implication rules for market basket data. In ACM SIGMOD International Conference On the Management of Data. 1997.
5 R Srik ant,R Agrawal.Mining generalized association rules[C].In:Proc of 21th Int’1 Conf on very Large Data Bases,Zurich,Switerland;Morgan Kaufmann,1995:407-419.
页数 9 字数 7791
摘要 本文介绍了关联规则挖掘的研究情况，提出了关联规则的分类方法，对一些典型算法进行了分析和评价，指出传统关联规则衡量标准的不足，归纳出关联规则的价值衡量方法，展望了关联规则挖掘的未来研究方向。
关键词 数据挖掘,关联规则,频集,OLAP
Abstract This paper provides a survey of the study in association rule generation,brings forward a classification of association rule,reviews and analyses some typical algorithms,points out the weakness of the traditional measure method,concludes the measure method of the association rule’s value,views some future directions in association rule generation.
Key Words Data Mining, Association Rules, Large Itemset,OLAP
参考文献
1 R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD Conference on Management of data, pp. 207-216, 1993.
2 Aly HH,Taha Y, Amr AA. Fast mining of association rules in large-scale problems.In:Abdel-Wahab H,Jeffay K,eds.Proc.of the 6th IEEE Symp.on Computers and
Communications(ISCC 2001).New York:IEEE Computer Society Press,2001.107-113.
3 Tsai CF,Lin YC,Chen CP.A new fast algorithms for mining association rules in large databases.In:Kamel AE,Mellouli K,Borne.P,eds.Proc.of the 2002 IEEE Int’1 Confon Systems, Man and Cybernetics(SMC 2002).IEEE Computer Soceity Press,2002.251-256.
4 S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic Itemset counting and implication rules for market basket data. In ACM SIGMOD International Conference On the Management of Data. 1997.
5 R Srik ant,R Agrawal.Mining generalized association rules[C].In:Proc of 21th Int’1 Conf on very Large Data Bases,Zurich,Switerland;Morgan Kaufmann,1995:407-419.
