Discovering of association rules without a minimum support threshold - coherent rules discovery
暂无分享,去创建一个
[1] Wynne Hsu,et al. Mining association rules with multiple minimum supports , 1999, KDD '99.
[2] Yen-Liang Chen,et al. Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism , 2004, Decision Support Systems.
[3] Alex Tze Hiang Sim,et al. Importance of Negative Associations and Mining of Association Pairs , 2007, iiWAS.
[4] Anthony K. H. Tung,et al. Mining top-K covering rule groups for gene expression data , 2005, SIGMOD '05.
[5] Tetsuya Murai,et al. A Note on Conditional Logic and Association Rules , 2001, JSAI Workshops.
[6] Geoffrey I. Webb. Discovering significant rules , 2006, KDD '06.
[7] Usama M. Fayyad,et al. Knowledge Discovery in Databases: An Overview , 1997, ILP.
[8] Ansaf Salleb-Aouissi,et al. An Application of Association Rules Discovery to Geographic Information Systems , 2000, PKDD.
[9] J. Hardin,et al. Association rules and data mining in hospital infection control and public health surveillance. , 1998, Journal of the American Medical Informatics Association : JAMIA.
[10] Byung-Do Kim,et al. Market Basket Analysis , 2008 .
[11] Petra Perner,et al. Data Mining - Concepts and Techniques , 2002, Künstliche Intell..
[12] Xiangjun Dong,et al. Study of Positive and Negative Association Rules Based on Multi-confidence and Chi-Squared Test , 2006, ADMA.
[13] Rajeev Motwani,et al. Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.
[14] Dimitrios Gunopulos,et al. Constraint-Based Rule Mining in Large, Dense Databases , 2004, Data Mining and Knowledge Discovery.
[15] Jianhong Wu,et al. Association Bundle - A New Pattern for Association Analysis , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).
[16] Philip S. Yu,et al. Scoring the Data Using Association Rules , 2003, Applied Intelligence.
[17] Jeffrey F. Naughton,et al. Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data , 1980, SIGMOD 2000.
[18] Xingquan Zhu,et al. Quantitative Association Rules , 2009, Encyclopedia of Database Systems.
[19] Georgios C. Anagnostopoulos,et al. Knowledge-Based Intelligent Information and Engineering Systems , 2003, Lecture Notes in Computer Science.
[20] Ya-Han Hu,et al. 在多重支持度下有效率的挖掘與維護關聯規則; An Efficient Algorithm for Discovery and Maintenance of Frequent Patterns with Multiple Minimum Supports , 2003 .
[21] F. Kviz,et al. Interpreting Proportional Reduction in Error Measures as Percentage of Variation Explained , 1981 .
[22] L.N. Alachaher,et al. Mining Negative and Positive Influence Rules Using Kullback-Leibler Divergence , 2007, 2007 International Multi-Conference on Computing in the Global Information Technology (ICCGI'07).
[23] Szymon Jaroszewicz,et al. Interestingness of frequent itemsets using Bayesian networks as background knowledge , 2004, KDD.
[24] Wynne Hsu,et al. Analyzing the Subjective Interestingness of Association Rules , 2000, IEEE Intell. Syst..
[25] Stamatios V. Kartalopoulos,et al. Proceedings of the 12th WSEAS international conference on Computers , 2008 .
[26] Keun Ho Ryu,et al. Mining association rules on significant rare data using relative support , 2003, J. Syst. Softw..
[27] Xindong Wu,et al. Mining Both Positive and Negative Association Rules , 2002, ICML.
[28] Chris Cornelis,et al. Mining Positive and Negative Fuzzy Association Rules , 2004, KES.
[29] Geoffrey I. Webb,et al. Mining Negative Rules Using GRD , 2004, PAKDD.
[30] Yanchun Zhang,et al. Direct interesting rule generation , 2003, Third IEEE International Conference on Data Mining.
[31] Hiep Xuan Huynh,et al. Evaluating Interestingness Measures with Linear Correlation Graph , 2006, IEA/AIE.
[32] Tobias Scheffer,et al. Finding association rules that trade support optimally against confidence , 2001, Intell. Data Anal..
[33] Rajeev Motwani,et al. Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.
[34] Stéphane Bressan,et al. Application of association rules mining to Named Entity Recognition and co-reference resolution for the Indonesian language , 2007, Int. J. Bus. Intell. Data Min..
[35] Tushar Mani,et al. Mining Negative Association Rules , 2012 .
[36] Jiuyong Li,et al. On optimal rule discovery , 2006, IEEE Transactions on Knowledge and Data Engineering.
[37] Earl R. Babbie,et al. Adventures in social research : data analysis using SPSS 11.0/11.5 for Windows , 2003 .
[38] Ramakrishnan Srikant,et al. Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.
[39] David Taniar,et al. Exception Rules Mining Based on Negative Association Rules , 2004, ICCSA.
[40] Philip S. Yu,et al. Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.
[41] Carla E. Brodley,et al. KDD-Cup 2000 organizers' report: peeling the onion , 2000, SKDD.
[42] Ke Wang,et al. Growing decision trees on support-less association rules , 2000, KDD '00.
[43] Wen-Yang Lin,et al. A Confidence-Lift Support Specification for Interesting Associations Mining , 2002, PAKDD.
[44] Sylvia Encheva,et al. Application of association rules in education , 2006 .
[45] U. M. Feyyad. Data mining and knowledge discovery: making sense out of data , 1996 .
[46] Bala Srinivasan,et al. Mining infrequent and interesting rules from transaction records , 2008 .
[47] Tetsuya Murai,et al. Association rules and non-classical logics , 2002, Proceedings 26th Annual International Computer Software and Applications.
[48] Jeffrey D. Ullman,et al. A Survey of Association-Rule Mining , 2000, Discovery Science.
[49] Rajeev Motwani,et al. Beyond Market Baskets: Generalizing Association Rules to Dependence Rules , 1998, Data Mining and Knowledge Discovery.
[50] Sudha Ram,et al. Proceedings of the 1997 ACM SIGMOD international conference on Management of data , 1997, ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems.
[51] Abraham Silberschatz,et al. On Subjective Measures of Interestingness in Knowledge Discovery , 1995, KDD.
[52] Shusaku Tsumoto,et al. Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence , 2001 .
[53] Jian Pei,et al. Mining frequent patterns without candidate generation , 2000, SIGMOD '00.
[54] T. Mcintosh,et al. High Confidence Rule Mining for Microarray Analysis , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[55] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[56] Gregory Piatetsky-Shapiro,et al. Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.
[57] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.
[58] Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search , 1995, J. Artif. Intell. Res..
[59] Philip S. Yu,et al. A new framework for itemset generation , 1998, PODS '98.
[60] Philip W. Goetz. The New Encyclopaedia Britannica , 1991 .
[61] Edward Omiecinski,et al. Alternative Interest Measures for Mining Associations in Databases , 2003, IEEE Trans. Knowl. Data Eng..
[62] Weihua Wu,et al. Mining confident minimal rules with fixed-consequents , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.
[63] Osmar R. Zaïane,et al. Incremental mining of frequent patterns without candidate generation or support constraint , 2003, Seventh International Database Engineering and Applications Symposium, 2003. Proceedings..
[64] Maria Indrawan,et al. A threshold free implication rule mining , 2008, ICDM 2008.
[65] Geert Wets,et al. Using association rules for product assortment decisions: a case study , 1999, KDD '99.
[66] Spiridon D. Likothanassis,et al. Mutual Information Clustering for Efficient Mining of Fuzzy Association Rules with Application to Gene Expression Data Analysis , 2005, Int. J. Artif. Intell. Tools.
[67] Philippe Lenca,et al. A Clustering of Interestingness Measures , 2004, Discovery Science.
[68] Régis Gras,et al. Assessing rule interestingness with a probabilistic measure of deviation from equilibrium , 2005 .
[69] Xindong Wu,et al. Efficient mining of both positive and negative association rules , 2004, TOIS.
[70] Jianning Dong,et al. The application of association rule mining to remotely sensed data , 2000, SAC '00.
[71] Bala Srinivasan,et al. The importance of negative associations and the discovery of association rule pairs , 2008, Int. J. Bus. Intell. Data Min..
[72] Naveen Kumar,et al. Data Mining for Business Intelligence–Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner® , 2012 .
[73] Ramakrishnan Srikant,et al. Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.
[74] Jaideep Srivastava,et al. Selecting the right interestingness measure for association patterns , 2002, KDD.
[75] Ming Zhao,et al. Research on Application of Improved Association Rules Algorithm in Intelligent QA System , 2008, 2008 Second International Conference on Genetic and Evolutionary Computing.
[76] Stefan Wrobel,et al. An Algorithm for Multi-relational Discovery of Subgroups , 1997, PKDD.
[77] Chava Nachmias,et al. Social statistics for a diverse society , 2009 .
[78] Xuan-Hiep Huynh,et al. ARQAT : an exploratory analysis tool for interestingness measures , 2005 .
[79] Patrick Meyer,et al. Association Rule Interestingness Measures: Experimental and Theoretical Studies , 2007, Quality Measures in Data Mining.
[80] Yun Sing Koh,et al. Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse , 2006, Int. J. Data Warehous. Min..
[81] Howard J. Hamilton,et al. Interestingness measures for data mining: A survey , 2006, CSUR.
[82] Guangjun Song. The Research of Association Rules Mining and Application in Intrusion Alerts Analysis , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).
[83] Warren T. Jones. Public health surveillance using association rules , 1998, SIGB.
[84] Guoqing Chen,et al. Mining Positive and Negative Association Rules from Large Databases , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.
[85] Osmar R. Zaïane,et al. Mining Positive and Negative Association Rules: An Approach for Confined Rules , 2004, PKDD.
[86] J. Kalita,et al. Horizontal vs . Vertical Partitioning in Association Rule Mining : A Comparison , 2003 .
[87] Yun Sing Koh,et al. Mining interesting imperfectly sporadic rules , 2006, Knowledge and Information Systems.
[88] Min Gan,et al. Extended Negative Association Rules and the Corresponding Mining Algorithm , 2005, ICMLC.
[89] Stephen D. Bay,et al. Detecting change in categorical data: mining contrast sets , 1999, KDD '99.
[90] L. A. Goodman,et al. Measures of association for cross classifications , 1979 .
[91] Barrett R. Bryant,et al. Proceedings of the 2000 ACM symposium on Applied computing - Volume 2 , 2000 .
[92] Earl R. Babbie,et al. Adventures in Social Research: Data Analysis Using SPSS for Windows/Book and Disk , 1993 .
[93] Raymond Chi-Wing Wong,et al. Data Mining for Inventory Item Selection with Cross-Selling Considerations , 2005, Data Mining and Knowledge Discovery.
[94] Yue-Shi Lee,et al. Mining Interesting Association Rules: A Data Mining Language , 2002, PAKDD.
[95] Geoffrey I. Webb,et al. K-Optimal Rule Discovery , 2005, Data Mining and Knowledge Discovery.
[96] Tetsuya Murai,et al. Association Rules and Dempster-Shafer Theory of Evidence , 2003, Discovery Science.
[97] Shichao Zhang,et al. Association Rule Mining: Models and Algorithms , 2002 .
[98] Yun Sing Koh,et al. Finding Sporadic Rules Using Apriori-Inverse , 2005, PAKDD.
[99] Jörg Flum,et al. Mathematical logic (2. ed.) , 1994, Undergraduate texts in mathematics.
[100] Jiawei Han,et al. Mining top-k frequent closed patterns without minimum support , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[101] N. Gogtay,et al. Measures of Association. , 2016, The Journal of the Association of Physicians of India.