摘要
给出了一个基于候选间接关联反单调性和频繁项目对支持矩阵的不需要生成所有频繁集的直接挖掘项目对之间间接关联的挖掘算法,并在一个Web log的真实数据集上进行了试验,与现有算法的比较表明该算法具有更好的性能。
出处
《高技术通讯》
EI
CAS
CSCD
2004年第7期49-52,共4页
Chinese High Technology Letters
参考文献9
-
1Agrawal R,Imielinski T,Swami A.Mining association rules between sets of items in large databases.In:Proc ACM SIGMOD Conference on Management of Data.Washington D C,1993.207 被引量:2
-
2Melamed D.Automatic construction of clean broad-coverage translation lexicons.In:2nd Conference of the Association for Machine Translation in the Americas (ATMA 96).Bombay,India,1996 被引量:2
-
3Tan P N,Kumar V,Srivastava J.Indirect association:mining higher order dependencies in data.In:PKDD 2000.Lyon,2000.632 被引量:2
-
4Tan P N,Kumar V,Kuno H.Using SAS for mining indirect associations in data.In:Proc of the Western Users of SAS Software Conference.Los Angeles,2001 被引量:2
-
5Tan P N,Kumar V.Mining indirect associations in web data.In:WebKDD 2001.San Francisco,2001 被引量:2
-
6Brin S,Motwani R,Silverstein C.Beyond market baskets:generalizing association rules to correlations.In:Proc ACM SIGMOD Intl Conf Management of Data.AZ:Tucson,1997.265 被引量:1
-
7Blake C L,Merz C J.UCI repository of machine learning databases.http://kdd.ics.uci.edu/databases/msweb/msweb.data.html 被引量:1
-
8Breese J S,Heckerman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering.In:Proc Fourteenth Conference on Uncertainty in Artificial Intelligence(UAI'98).Madison,Wisconsin,1998 被引量:1
-
9Zheng Z,Kohavi R,Mason L.Real world performance of association rule algorithms.In:KDD-2001.San Francisco,2001.401 被引量:1
同被引文献12
-
1陈安龙,唐常杰,陶宏才,元昌安,谢方军.基于极大团和FP-Tree的挖掘关联规则的改进算法[J].软件学报,2004,15(8):1198-1207. 被引量:30
-
2Ceglar A, Roddick J F. Association mining. ACM Computing Surveys, 2006, 38(2): 1-42. 被引量:1
-
3Agrawal R, lmielinski T, Swami A. Mining associations between sets of items in massive databases. In: Proceedings of the 1993 ACM SIGMOD Intematiorial Conference on Management of Data. New York: ACM Press, 1993. 207-216. 被引量:1
-
4Agrawal R, Srikant R. Fast algorithms for mining association rules. In : Proceedings of the 20th International Conference on Very Large Data Bases. San Francisco, CA: Morgan Kaufmann, 1994. 487-499. 被引量:1
-
5Park J S, Chen M S, Yu P S. An efficient hash-based algorithm for mining association rules. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 1995. 175-186. 被引量:1
-
6Toivonen H. Sampling large databases for association rules. In: Proceedings of 22th International Conference on Very large Data Bases. San Francisco, CA: Morgan Kaufmann, 1996. 134-145. 被引量:1
-
7Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM-SIGMOD International Conference on Management of Data. New York: ACM Press, 2000. 1-12. 被引量:1
-
8Liu G M, I,u H J, Lou W W el al. Efficient mining of frequent patterns using ascending frequency ordered prefix-tree. Data Mining and Knowledge Discovery, 2004, 9 ( 3 ) : 249- 274. 被引量:1
-
9Grahne G, Zhu J. Fast algorithms for frequent itemset inining using FP-trees. IEEE Transaction on Knowledge and Data Engineering, 2005, 17(10) : 1347-1362. 被引量:1
-
10Zaki M J. Scalable algorithms for association mining. IEEE Transactions on. Knowledge and Data Engineering, 2000, 12 (3) : 372-390. 被引量:1
-
1倪旻,徐晓飞,邓胜春,问晓先.TRISCAN-IA:一种间接关联挖掘的快速算法[J].哈尔滨工业大学学报,2004,36(5):578-581.
-
2陈乐然,王刚,陈威,徐小天.VMware虚拟化环境安全风险与防护方案研究[J].华北电力技术,2014(9):61-65. 被引量:3
-
3倪旻,徐晓飞,邓胜春,赵政.基于频繁项目对支持矩阵的Apriori优化算法[J].小型微型计算机系统,2004,25(5):872-874. 被引量:6
-
4杨明,杨萍.一种基于前缀广义表的快速间接关联挖掘算法[J].安徽工程科技学院学报(自然科学版),2004,19(4):40-45.
-
5段巧灵,李芬,张莉.多数据库中的间接关联规则挖掘算法[J].软件导刊,2016,15(9):49-51. 被引量:1
-
6薄宏,任玉杰,曹惠茹.基于间接关联规则的数据挖掘算法研究[J].计算机技术与发展,2012,22(11):120-122. 被引量:1
-
7林尤舜,钟声.基于RBAC的权限管理系统的设计与实现[J].现代机械,2009(3):59-60. 被引量:3
-
8李亚楠,许晟,王斌.基于加权SimRank的中文查询推荐研究[J].中文信息学报,2010,24(3):3-10. 被引量:15
-
9李瑞敏,林鸿飞,闫俊.基于用户-标签-项目语义挖掘的个性化音乐推荐[J].计算机研究与发展,2014,51(10):2270-2276. 被引量:42
-
10冯锦丹,战德臣,聂兰顺,徐晓飞.ICEMDA中的业务对象关联和状态管理[J].高技术通讯,2012,22(3):326-334.