Interestingness measures for association rules based on statistical validity

Knowledge-Based Systems

Volumn 24, Issue 3, 2011, Pages 386-392

  Shaharanee, Izwan Nizal Mohd ^a   Hadzic, Fedja ^a   Dillon, Tharam S ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

Data mining; Interesting rules; Interestingness measure; Redundant rules; Statistical analysis; Structured data

Indexed keywords

INTERESTING RULES; INTERESTINGNESS MEASURES; REDUNDANT RULES; STATISTICAL ANALYSIS; STRUCTURED DATA;

ASSOCIATION RULES; DATA HANDLING; DATA MINING; SAMPLING; STATISTICS;

MULTIVARIANT ANALYSIS;

EID: 79551689085     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2010.11.005     Document Type: Article

References (35)

1. C.C. Aggarwal, P.S. Yu, A new framework for itemset generation, in: Proceedings of the 17th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Seattle, Washington, United States, 1998.
2. R. Agrawal, T. Imieliski, and A. Swami Mining association rules between sets of items in large databases SIGMOD Rec. 22 1993 207 216
3. R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, in: Proceedings of the 20th International Conference on Very Large Databases, 1994.
4. A. Agresti An Introduction to Categorical Data Analysis second ed. 2007 Wiley-Interscience Hoboken, N.J
5. Y. Aumann, and Y. Lindell A statistical theory for quantitative association rules J. Intell. Inf. Syst. 20 2003 255 283
6. T. AydIn, and H.A. Güvenir Modeling interestingness of streaming association rules as a benefit-maximizing classification problem Knowledge Based Syst. 22 2009 85 99
7. S.D. Bay, and M.J. Pazzani Detecting group differences: mining contrast sets Data Min. Knowledge Discovery 5 2001 213 246
8. R.J. Bayardo, R. Agrawal, and D. Gunopulos Constraint-based rule mining in large, dense databases Data Min. Knowledge Discovery 4 2000 217 240
9. L. Bing, H. Wynne, and M. Yiming Mining Association Rules with Multiple Minimum Supports Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1999 San Diego California, United States
10. J. Blanchard, F. Guillet, R. Gras, H. Briand, Using information-theoretic measures to assess association rule interestingness, in: Proceedings of the 5th IEEE International Conference on Data Mining, 2005.
11. H. Cheng, X. Yan, J. Han, and P.S. Yu Direct Discriminative Pattern Mining for Effective Classification 2008 Piscataway NJ, USA pp. 169-178
12. T.S. Dillon, T. Hossain, W. Bloomer, M. Witten, Improvements in supervised brainne: a method for symbolic data mining using neural networks, in: S. Spaccapietra, F.J. Maryanski (Eds.), IFIP TC2/WG2.6 Seventh Conference on Database Semantics (DS-7), Leysin, Switzerland, 1998, pp. 67-88.
13. A.A. Freitas On rule interestingness measures Knowledge Based Syst. 12 1999 309 315
14. L. Geng, and H.J. Hamilton Interestingness measures for data mining: a survey ACM Comput. Surv. 38 2006 9
15. A. Goodman, C. Kamath, and V. Kumar Data analysis in the 21st century Stat. Anal. Data Min. 1 2008 1 3
16. W. Hämäläinen, M. Nykänen, Efficient discovery of statistically significant association rules, in: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, 2008.
17. J. Han, and M. Kamber Data Mining: Concepts and Techniques 2001 Morgan Kaufmann San Francisco
18. D.W. Hosmer, and S. Lemeshow Applied Logistic Regression 1989 Wiley New York
19. A. Kirsch, M. Mitzenmacher, A. Pietracaprina, G. Pucci, E. Upfal, F. Vandin, An efficient rigorous approach for identifying statistically significant frequent itemsets, in: Proceedings of the 28th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Providence, Rhode Island, USA, 2009.
20. S. Lallich, O. Teytaud, and E. Prudhomme Association rule interestingness: measure and statistical validation Qual. Meas. Data Min. 2007 251 275
21. N. Lavrač, P. Flach, B. Zupan, Rule Evaluation Measures: A Unifying View, Inductive Logic Programming. pp. 174-185.
22. J. Li, H. Shen, and R.W. Topor Mining the optimal class association rule set Knowledge Based Syst. 15 2002 399 405
23. S. Lotfi, and M.H. Sadreddini Mining Fuzzy Association Rules Using Mutual Information 2009 International MultiConference of Engineers and Computer Scientists Publishing Hong Kong
24. K. McGarry A survey of interestingness measures for knowledge discovery Knowl. Eng. Rev. 20 2005 39 61
25. N. Meggido, R. Srikant, Discovering predictive association rules, in: 4th International Conference on Knowledge Discovery in Databases and Data Mining, Publishing, 1998, pp. 274-278.
26. I.N. Mohd. Shaharanee, F. Hadzic, T. Dillon, Interestingness of association rules using symmetrical tau and logistic regression, in: A. Nicholson, X. Li (Eds.), AI 2009, 2009, pp. 442-431.
27. I.N.M. Shaharanee, T.S. Dillon, F. Hadzic, Ascertaining association rules using statistical analysis, in: P.S. Sandhu (Ed.), 2009 International Symposium on Computing, Communication and Control (ISCCC 2009), Singapore, 2009, pp. 180-188.
28. P.-N. Tan, V. Kumar, J. Srivastava, Selecting the right interestingness measure for association patterns, in: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 2002.
29. G.I. Webb, Preliminary investigations into statistically valid exploratory rule discovery, in: Australasian Data Mining Workshop (AudDM03), 2003, pp. 1-9.
30. G.I. Webb Discovering Significant Patterns Machine Learning 2007 Springer pp. 1-33
31. J.-M. Wei, W.-G. Yi, and M.-Y. Wang Novel measurement for mining effective association rules Knowledge Based Syst. 19 2006 739 743
32. C. Weiß Statistical mining of interesting association rules Stat. Comput. 18 2008 185 194
33. H. Yun, D. Ha, B. Hwang, and K.H. Ryu Mining association rules on significant rare data using relative support J. Syst. Softw. 67 2003 181 191
34. H. Zhang, B. Padmanabhan, A. Tuzhilin, On the discovery of significant statistical quantitative rules, in: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA, USA, 2004.
35. X.J. Zhou, and T.S. Dillon A statistical-heuristic feature selection criterion for decision tree induction IEEE Trans. Pattern Anal. Mach. Intell. 13 1991 834 841