摘要: Self-sufficient itemsets are those whose frequency cannot be explained solely by the of either their subsets or supersets. We argue that not self-sufficient will often little interest to data analyst, as should expected once on which depends is known. present tests for statistically sound discovery itemsets, and computational techniques allow applied a post-processing step any itemset algorithm. also measure assessing degree potential in an complements these statistical measures.
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sci-hub.se 参考文章(36)
Nimrod Megiddo, Ramakrishnan Srikant, Discovering predictive association rules knowledge discovery and data mining. pp. 274- 278 ,(1998)
Linda S. Fidell, Barbara G. Tabachnick, SAS for Windows workbook for Tabachnick and Fidell : using multivariate statistics Allyn and Bacon. ,(2001)
Barbara G Tabachnick, Linda S Fidell, Jodie B Ullman, None, Using multivariate statistics ,(1983)
Yves Bastide, Nicolas Pasquier, Rafik Taouil, Gerd Stumme, Lotfi Lakhal, Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets Lecture Notes in Computer Science. ,vol. 1861, pp. 972- 986 ,(2000) , 10.1007/3-540-44957-4_65
Mohammed Javeed Zaki, Ching-Jiu Hsiao, CHARM : An Efficient Algorithm for Closed Itemset Mining siam international conference on data mining. pp. 457- 473 ,(2002)
Gregory Piatetsky-Shapiro, Discovery, Analysis, and Presentation of Strong Rules Knowledge Discovery in Databases. pp. 229- 238 ,(1991)
Robert Cooley, Pang-Ning Tan, Jaideep Srivastava, Discovery of Interesting Usage Patterns from Web Data Web Usage Analysis and User Profiling. pp. 163- 182 ,(2000) , 10.1007/3-540-44934-5_10
Hong Yao, Howard J. Hamilton, Mining itemset utilities from transaction databases data and knowledge engineering. ,vol. 59, pp. 603- 626 ,(2006) , 10.1016/J.DATAK.2005.10.004
Szymon Jaroszewicz, Dan A. Simovici, Interestingness of frequent itemsets using Bayesian networks as background knowledge knowledge discovery and data mining. pp. 178- 186 ,(2004) , 10.1145/1014052.1014074
Yonatan Aumann, Yehuda Lindell, A statistical theory for quantitative association rules knowledge discovery and data mining. pp. 261- 270 ,(1999) , 10.1145/312129.312243