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References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. Proceedings of the ACM-SIGMOD International Conference on Management of Data. Washington DC, USA (1993) 207-216
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. VLDB’94, Proceedings of the 20th International Conference on Very Large Data Bases. Santiago, Chile (1994) 487-499
Agrawal, R., Mannila, H., Srikant, R., Toivonen H., Verkano, A.I.: Fast discovery of association rules. Advances in Knowledge Discovery in Databases. (1996) 307-328
Azé, J., Kodratoff, Y.: A study of the Effect of Noisy Data in Rule Extraction Systems. EMCSR’02, Proceedings of the Sixteenth European Meeting on Cybernetics and Systems Research. (2002) 781-786
Bayardo, Jr.R.J., Agrawal, R.: Mining the most interesting rules. KDD’99, Proceedings of the Fifth ACM SIGKDD international conference on Knowledge discovery and data mining. San Diego, CA, USA (1999) 145-154
Blanchard, J., Guillet, F., Gras, R., and Briand, H.: Using information-theoretic measures to assess association rule interestingness. ICDM’05, Proceedings of the 5th IEEE Internationl Confereance on Data Mining, IEEE Computer Society Press, (2005) 66-73.
Blanchard, J., Guillet, F., Gras, R., Briand, H.: Assessing rule interestingness with a probabilistic measure of deviation from equilibrium. ASMDA’05, Proceedings of the 11th International Symposium on Applied Stochastic Models and Data Analysis. (2005) 191-200
Blanchard, J., Guillet, F., Gras, R., Briand, H.: Mesurer la qualité des règles et de leurs contraposées avec le taux informationnel TIC. EGC’04, Actes de 4èmes journées d’Extraction et de Gestion des Connaissances, RNTI-E-2, Vol. 1. Cépaduès Editions, Clermont Ferrand, France (2004) 287-298 (in French)
Blanchard, J., Kuntz, P., Guillet, F., Gras, R.: Implication Intensity: from the basic statistical definition to the entropic version. Statistical Data Mining and Knowledge Discovery, Chapter 28. Chapman & Hall, CRC Press (2003) 475-493
Freitas, A.A.: On rule interestingness measures. Knowledge-Based Systems, 12 (5-6). (1999) 309-315
Gavrilov, M., Anguelov, D., Indyk, P., and Motwani, R.: Mining the stock market: which measure is best?. KDD’00, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining . Boston, MA, USA (2000) 487-496.
Gras, R., Couturier, R., Blanchard, J., Briand, H., Kuntz, P., Peter, P.: Quelques critères pour une mesure de qualité de règles d’association. Mesures de Qualité pour la Fouille de Données, RNTI-E-1. Cépaduès Editions (2004) 3-31 (in French)
Gras, R.: L’implication statistique - Nouvelle méthode exploratoire de données. La Pensée Sauvage Édition (1996) (in French)
Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interestingness. Kluwer Academic Publishers (2001)
Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., and Verkano, A.I.: Finding interesting rules from larges sets of discovered association rules. ICIKM’94, Proceedings of the Third International Conference on Information and Knowledge Management. Ed. Nabil R. Adam, Bharat K. Bhargava and Yelena Yesha, Gaithersburg, Maryland. ACM Press, (1994) 401-407.
Huynh, X.-H., Guillet, F., Briand, H.: Clustering interestingness measures with positive correlation. ICEIS’05, Proceedings of the 7th International Conference on Enterprise Information Systems. (2005) 248-253
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York, (1990)
Kodratoff, Y.: Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts. Machine Learning and Its Applications, LNCS 2049. Springer-Verlag, (2001) 1-21
Kononenco, I.: On biases in estimating multi-valued attributes. IJCAI’95. (1995) 1034-1040
Lenca, P., Lallich, S., Vaillant, B.: On the robustness of association rules. Proceedings of the IEEE International Conference on Cybernetics and Intelligent Systems. (2006) 596-601
Liu, B., Hsu, W., Mun, L., Lee, H.: Finding interestingness patterns using user expectations. IEEE Transactions on Knowledge and Data Mining (11). (1999) 817-832
Loevinger, J.: A systematic approach to the construction and evaluation of tests of ability. Psychological Monographs. (1947)
Newman, D.J., Hettich,S., Blake, C.L., Merz, C.J.: [UCI] Repository of machine learning databases, http://www.ics.uci.edu/∼mlearn/MLRepository.html. University of California, Irvine, Department of Information and Computer Sciences, (1998).
Padmanabhan, B., Tuzhilin, A. : A belief-driven method for discovering unexpected patterns. KDD’98, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. (1998) 94-100
Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W. Frawley editors. MIT Press, Cambridge, MA (1991) 229-248
Piatetsky-Shapiro, G., Steingold, S.: Measuring Lift Quality in Database Marketing. SIGKDD Explorations 2(2). (2000) 76-80
Ross, S.M.: Introduction to probability and statistics for engineers and scientists. Wiley, (1987)
Sebag, M., Schoenauer, M.: Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases. EKAW’88, Proceedings of the European Knowledge Acquisition Workshop. Gesellschaft fr Mathematik und Datenverarbeitung mbH (1988) 28.1-28.20
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge Data Engineering 8(6). (1996) 970-974
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Information Systems 29(4). (2004) 293-313
Vaillant, B., Lenca, P., Lallich, S.: A clustering of interestingness measures. DS’04, the 7th International Conference on Discovery Science LNAI 3245. (2004) 290-297
Vaillant, B., Lallich, S., Lenca, P.: Modeling of the counter-examples and association rules interestingness measures behavior. The 2006 International Conference on Data Mining. (2006)
Zhao, Y., Karypis, G.: Criterion functions for document clustering: experiments and analysis. Technical Report TR01-40, Deparment of Computer Science, University of Minnesota. (2001) 1-30
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Huynh, XH., Guillet, F., Blanchard, J., Kuntz, P., Briand, H., Gras, R. (2007). A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study. In: Guillet, F.J., Hamilton, H.J. (eds) Quality Measures in Data Mining. Studies in Computational Intelligence, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44918-8_2
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