Interestingness of association rules in data mining: Issues relevant to e-commerce
暂无分享,去创建一个
[1] Alex Alves Freitas,et al. On Objective Measures of Rule Surprisingness , 1998, PKDD.
[2] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[3] Ke Wang,et al. Interestingness-Based Interval Merger for Numeric Association Rules , 1998, KDD.
[4] Philip S. Yu,et al. Mining Associations with the Collective Strength Approach , 2001, IEEE Trans. Knowl. Data Eng..
[5] Ramayya Krishnan,et al. E-Business and Management Science: Mutual Impacts (Part 2 of 2) , 2003, Manag. Sci..
[6] Ron Kohavi,et al. Integrating e-commerce and data mining: architecture and challenges , 2000, Proceedings 2001 IEEE International Conference on Data Mining.
[7] Sigal Sahar,et al. Exploring interestingness through clustering: a framework , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[8] Gediminas Adomavicius,et al. Expert-Driven Validation of Rule-Based User Models in Personalization Applications , 2004, Data Mining and Knowledge Discovery.
[9] Howard J. Hamilton,et al. Visualizing data mining results with domain generalization graphs , 2001 .
[10] Christopher M. Bishop,et al. Classification and regression , 1997 .
[11] F RoddickJohn,et al. What's interesting about Cricket? , 2001 .
[12] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.
[13] Dan A. Simovici,et al. Generating an informative cover for association rules , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[14] Narsingh Deo,et al. Graph Theory with Applications to Engineering and Computer Science , 1975, Networks.
[15] Sridhar Ramaswamy,et al. Cyclic association rules , 1998, Proceedings 14th International Conference on Data Engineering.
[16] Gregory Piatetsky-Shapiro,et al. Measuring lift quality in database marketing , 2000, SKDD.
[17] Ramasamy Uthurusamy,et al. EVOLVING DATA MINING INTO SOLUTIONS FOR INSIGHTS , 2002 .
[18] Joydeep Ghosh,et al. Distance based clustering of association rules , 1999 .
[19] Andreas Rudolph,et al. Techniques of Cluster Algorithms in Data Mining , 2002, Data Mining and Knowledge Discovery.
[20] Sumit Sarkar,et al. The Role of the Management Sciences in Research on Personalization , 2003, Manag. Sci..
[21] Michael R. Anderberg,et al. Cluster Analysis for Applications , 1973 .
[22] Gediminas Adomavicius,et al. Discovery of Actionable Patterns in Databases: the Action Hierarchy Approach , 1997, KDD.
[23] Ming-Syan Chen,et al. On the mining of substitution rules for statistically dependent items , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[24] Sigal Sahar,et al. Interestingness via what is not interesting , 1999, KDD '99.
[25] Rajeev Motwani,et al. Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.
[26] Rosa Meo. Theory of dependence values , 2000, TODS.
[27] Wynne Hsu,et al. Finding Interesting Patterns Using User Expectations , 1999, IEEE Trans. Knowl. Data Eng..
[28] Padhraic Smyth,et al. From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.
[29] Ron Kohavi,et al. Applications of Data Mining to Electronic Commerce , 2000, Springer US.
[30] Jaideep Srivastava,et al. Selecting the right objective measure for association analysis , 2004, Inf. Syst..
[31] B. Shekar,et al. A Framework for Evaluating Knowledge-Based Interestingness of Association Rules , 2004, Fuzzy Optim. Decis. Mak..
[32] Alípio Mário Jorge. Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules , 2004, SDM.
[33] Balaji Padmanabhan,et al. Unexpectedness as a Measure of Interestingness in Knowledge Discovery , 1999, Decis. Support Syst..
[34] Edith Schonberg,et al. Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising , 2004, Data Mining and Knowledge Discovery.
[35] Ramayya Krishnan,et al. E-Business and Management Science: Mutual Impacts (Part 2 of 2) , 2003, Manag. Sci..
[36] Hongjun Lu,et al. Exception Rule Mining with a Relative Interestingness Measure , 2000, PAKDD.
[37] Mohammed J. Zaki. Generating non-redundant association rules , 2000, KDD '00.
[38] John Riedl,et al. Analysis of recommendation algorithms for e-commerce , 2000, EC '00.
[39] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[40] Hongjun Lu,et al. Beyond intratransaction association analysis: mining multidimensional intertransaction association rules , 2000, TOIS.
[41] A. Ram. Knowledge Goals : A Theory of Interestingness Ashwin , 1990 .
[42] Christos Faloutsos,et al. Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining , 1998, VLDB.
[43] Jennifer Widom,et al. SimRank: a measure of structural-context similarity , 2002, KDD.
[44] Ramasamy Uthurusamy,et al. Evolving data into mining solutions for insights , 2002, CACM.
[45] Wynne Hsu,et al. Multi-level organization and summarization of the discovered rules , 2000, KDD '00.
[46] N.R. Malik,et al. Graph theory with applications to engineering and computer science , 1975, Proceedings of the IEEE.
[47] Gregory Piatetsky,et al. Selecting and Reporting What is Interesting � The KEFIR Application to Healthcare Data , 2004 .
[48] Heikki Mannila,et al. Pruning and grouping of discovered association rules , 1995 .
[49] B. Shekar,et al. A transaction-based neighbourhood-driven approach to quantifying interestingness of association rules , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[50] Alex Alves Freitas,et al. On rule interestingness measures , 1999, Knowl. Based Syst..
[51] Heikki Mannila,et al. Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.
[52] Abraham Silberschatz,et al. What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..
[53] Jaideep Srivastava,et al. Selecting the right interestingness measure for association patterns , 2002, KDD.
[54] AdomaviciusGediminas,et al. Expert-Driven Validation of Rule-Based User Models in Personalization Applications , 2001 .
[55] M. Narasimha Murty,et al. Knowledge-based association rule mining using AND-OR taxonomies , 2003, Knowl. Based Syst..
[56] Andrew Whinston,et al. Frontiers of Electronic Commerce , 1996 .
[57] Dimitrios Gunopulos,et al. Constraint-Based Rule Mining in Large, Dense Databases , 2004, Data Mining and Knowledge Discovery.
[58] Bart Baesens,et al. Post-Processing of Association Rules , 2009 .
[59] John Riedl,et al. E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.
[60] Rajeev Motwani,et al. Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.
[61] Rajeev Motwani,et al. The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.
[62] Paul Resnick,et al. Recommender systems , 1997, CACM.
[63] John F. Roddick,et al. What's interesting about Cricket?: on thresholds and anticipation in discovered rules , 2001, SKDD.
[64] Andreas Geyer-Schulz,et al. Comparing Two Recommender Algorithms with the Help of Recommendations by Peers , 2002, WEBKDD.
[65] Ramakrishnan Srikant,et al. Mining generalized association rules , 1995, Future Gener. Comput. Syst..
[66] S. Burt,et al. E-commerce and the retail process: a review , 2003 .
[67] Shamkant B. Navathe,et al. Mining for strong negative associations in a large database of customer transactions , 1998, Proceedings 14th International Conference on Data Engineering.
[68] Peter J. Rousseeuw,et al. Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .
[69] Dimitrios Gunopulos,et al. Constraint-Based Rule Mining in Large, Dense Databases , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).
[70] John A. Major,et al. Selecting among rules induced from a hurricane database , 1993, Journal of Intelligent Information Systems.