Semantics-based classification of rule interestingness measures

Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction

Volumn , Issue , 2009, Pages 56-79

  Blanchard, Julien ^a   Guillet, Fabrice ^a   Kuntz, Pascale ^a  

^a UNIVERSITÉ DE NANTES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84873155427     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-60566-404-0.ch004     Document Type: Chapter

References (51)

1. Aggarwal, C. C., & Yu, P. S. (2001). Mining associations with the collective strength approach. IEEE Transactions on Knowledge and Data Engineering, 13(6), 863-873.
2. Agrawal, R., Imielienski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 SIGMOD international conference on management of data, (pp. 207-216). ACM Press.
3. Aze, J., & Kodratoff, Y. (2002). A study of the effect of noisy data in rule extraction systems. In Rappl, R. (Ed.), Proceedings of the Sixteenth European Meeting on Cybernetics and Systems Research (EMCSR'02), 2, 781-786.
4. Bayardo, R. J., & Agrawal, R. (1999). Mining the most interesting rules. In Proceedings of ACM KDD'1999, (pp. 145-154). ACM Press.
5. Blanchard, J. (2005). Un système de visualisation pour l'extraction, l'évaluation, et l'exploration interactives des règles d'association. PhD thesis, University of Nantes.
6. Blanchard, J., Guillet, F., Briand, H., & Gras, R. (2005a). Assessing rule interestingness with a probabilistic measure of deviation from equilibrium. In Proceedings of the 11th international symposium onApplied Stochastic Models and Data Analysis ASMDA-2005, (pp. 191-200).
7. Blanchard, J., Guillet, F., Gras, R., & Briand, H. (2005b). Using information-theoretic measures to assess association rule interestingness. In Proceedings of the fifth IEEE international conference on data mining ICDM'05, (pp. 66-73). IEEE Computer Society.
8. Blanchard, J., Kuntz, P., Guillet, F., & Gras, R. (2003). Implication intensity: from the basic statistical definition to the entropic version. In Statistical Data Mining and Knowledge Discovery, (pp. 473-485). Chapman & Hall.
9. Brin, S., Motwani, R., & Silverstein, C. (1997a). Beyond market baskets: Generalizing association rules to correlations. SIGMOD Record, 26(2), 265-276.
10. Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997b). Dynamic itemset counting and implication rules for market basket data. SIGMOD Record, 26(2), 255-264.
11. Clark, P., & Boswell, R. (1991). Rule induction with CN2: Some recent improvements. In EWSL'91: Proceedings of the European Working Session on Machine Learning, (pp. 151-163). Springer.
12. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, (20), 37-46.
13. Dice, L. (1945). Measures of the amount of ecologic association between species. Ecology, (26), 297-302.
14. Dunn, J. M. (1986). Relevance logic and entailment. In D. Gabbay, & F. Guenthner (Ed.), Hand-book of Philosophical Logic, 3, 117-224. Kluwer Academic Publishers.
15. Elder, J. F., & Pregibon, D. (1996). A statistical perspective on knowledge discovery in databases. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy, (Eds.), Advances in knowledge discovery and data mining, (pp. 83-113). AAAI/MIT Press.
16. Freitas, A. (1999). On rule interestingness measures. Knowledge-Based Systems Journal, 12(5-6), 309-315.
17. Ganascia, J. G. (1991). Deriving the learning bias from rule properties. In Machine intelligence 12: towards an automated logic of human thought, (pp. 151-167). Clarendon Press.
18. Geng, L., & Hamilton, H. J. (2007). Choosing the right lens: Finding what is interesting in data mining. In F. Guillet & H. J. Hamilton (Ed.), Quality Measures in Data Mining, volume 43 of Studies in Computational Intelligence, (pp. 3-24). Springer.
19. Gras, R. (1996). L'implication statistique: nouvelle méthode exploratoire de données. La Pensée Sauvage Editions.
20. Gras, R., & Kuntz, P. (2008). An overview of the statistical implicative analysis development. In Gras, R., Suzuki, E., Guillet, F., & Spagnolo, F. (Ed.), Statistical Implicative Analysis: Theory and Applications, volume 127 of Studies in Computational Intelligence, (pp. 21-52). Springer.
21. Huynh, X.-H., Guillet, F., & Briand, H. (2006). Evaluating interestingness measures with linear correlation graph. In Proceedings of the 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE), volume 4031 of Lecture Notes in Computer Science, (pp. 312-321). Springer.
22. Jaccard, P. (1901). Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, (37), 547-579.
23. Jaroszewicz, S., & Simovici, D. A. (2001). A general measure of rule interestingness. In Proceedings of PKDD'2001, (pp. 253-265). Springer.
24. Jeffreys, H. (1935). Some tests of significance treated by the theory of probability. In Proceedings of the Cambridge Philosophical Society, (pp. 203-222).
25. Kodratoff, Y. (2000). Extraction de connaissances à partir des données et des textes. In Actes des journées sur la fouille dans les données par la méthode d'analyse statistique implicative, (pp. 151-165). Presses de l'Université de Rennes 1.
26. Kodratoff, Y. (2001). Comparing machine learning and knowledge discovery in databases: an application to knowledge discovery in texts. In Paliouras, G., Karkaletsis, V., and Spyropoulos, C. (Ed.), Machine Learning and Its Applications, volume 2049 of Lecture Notes in Artificial Intelligence, (pp. 1-21). Springer.
27. Kulczynski, S. (1927). Die pflanzenassoziationen der pieninen. Bulletin International de l'Académie Polonaise des Sciences et des Lettres. Classe des Sciences Mathématiques et Naturelles, (suppl. II), 57-203. série B.
28. Lavrac, N., Flach, P. A., & Zupan, B. (1999). Rule evaluation measures: a unifying view. In ILP'99: Proceedings of the ninth International Workshop on Inductive Logic Programming, (pp. 174-185). Springer-Verlag.
29. Lenca, P., Vaillant, B., Meyer, P., & Lallich, S. (2007). Association rule interestingness measures: Experimental and theoretical studies. In Guillet, F. and Hamilton, H. J. (Ed.), Quality Measures in Data Mining, volume 43 of Studies in Computational Intelligence, (pp. 51-76). Springer.
30. Lerman, I. C. (1981). Classification et analyse ordinale des données. Dunod.
31. Lerman, I. C. (1993). Likelihood linkage analysis (LLA) classification method: An example treated by hand. Biochimie, 75(5), 379-397.
32. Liu, B., Hsu, W., Chen, S., & Ma, Y. (2000). Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, 15(5), 47-55.
33. Loevinger, J. (1947). A systematic approach to the construction and evaluation of tests of ability. Psychological Monographs, 61(4).
34. McGarry, K. (2005). A survey of interestingness measures for knowledge discovery. The Knowledge Engineering Review, 20(1), 39-61.
35. Mosteller, F. (1968). Association and estimation in contingency tables. Journal of the American Statistical Association, 63(321), 1-28.
36. Ochiai, A. (1957). Zoogeographic studies on the soleoid fishes found in japan and its neighbouring regions. Bulletin of the Japanese Society of Scientific Fisheries, (22), 526-530.
37. Padmanabhan, B., & Tuzhilin, A. (1999). Un-expectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, 27(3), 303-318.
38. Pearson, K. (1896). Mathematical contributions to the theory of evolution: regression, heredity and panmixia. Philosophical Transactions of the Royal Society Of London, series A(187), 253-318.
39. Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. In Knowledge Discovery in Databases, (pp. 229-248). AAAI/ MIT Press.
40. Quinlan, J. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.
41. Rogers, D., & Tanimoto, T. (1960). A computer program for classifying plants. Science, (132), 1115-1118.
42. Russel, P., & Rao, T. (1940). On habitat and association of species of anopheline larvae in southeastern madras. Journal of the Malaria Institute of India, (3):153-178.
43. Sebag, M., & Schoenauer, M. (1988). Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases. In Proceedings of EKAW88, (pp. 28.1-28.20).
44. Silberschatz, A., & Tuzhilin, A. (1996). User-assisted knowledge discovery: how much should the user be involved. In Proceedings of the 1996 SIGMOD workshop on research issues on data mining and knowledge discovery (DMKD).
45. Smyth, P., & Goodman, R. M. (1992). An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering, 4(4), 301-316.
46. Sokal, R., & Michener, C. (1958). A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin, (38), 1409-1438.
47. Tan, P.-N., & Kumar, V. (2000). Interestingness measures for association patterns: a perspective. In Proceedings of the KDD-2000 workshop on postprocessing in machine learning and data mining.
48. Tan, P.-N., Kumar, V., & Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems, 29(4), 293-313.
49. Yao, Y. Y., & Zhong, N. (1999). An analysis of quantitative measures associated with rules. In PAKDD'99: Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, pages 479-488. Springer.
50. Yule, G. (1900). On the association of attributes in statistics. Philosophical Transactions of the Royal Society of London, series A(194), 257-319.
51. Zembowicz, R., & Zytkow, J. M. (1996). From contingency tables to various forms of knowledge in databases. In Advances in knowledge discovery and data mining, (pp. 328-349). American Association for Artificial Intelligence.