A. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, Proc. ACM SIGMOD Intl. Conf. Management of Data, pp.94-105, 1998.

T. [. Agrawal, A. Imielinski, and . Swami, Mining association rules between sets of items in large databases, Proc. ACM SIGMOD Intl. Conf. Management of Data, pp.207-216, 1993.

Y. [. Azé and . Kodratoff, Évaluation de la résistance au bruit de quelques mesures d'extraction de règles d'association, pp.143-154, 2002.

H. R. Agrawal, R. Mannila, H. Srikant, A. I. Toivonen, and . Verkamo, Fast discovery of association rules, Advances in Knowledge Discovery and Data Mining, pp.307-328

S. [. Ali, R. Manganaris, and . Srikant, Partial classification using association rules, KDD, pp.115-118, 1997.

R. [. Agrawal and . Srikant, Fast algorithms for mining association rules in large databases, Proc. of the 20th Intl. Conf. on Very Large Data Bases (VLDB'94), pp.478-499, 1994.

P. [. Aggarwal and . Yu, A new framework for itemset generation, Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems , PODS '98, pp.18-24, 1998.
DOI : 10.1145/275487.275490

]. J. Azé03 and . Azé, Extraction de connaissances à partir de données numériques et textuelles, 2003.

T. [. Brachman and . Anand, The process of knowledge discovery in databases, Advances in Knowledge Discovery and Data Mining, pp.37-57

]. J. Bez81 and . Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, 1981.

J. Blanchard, F. Guillet, H. Briand, and R. Gras, Ipee : Indice probabiliste d'écart á l'équilibre pour l'évaluation de la qualité des règles, Atelier Qualité des Données et des Connaissances, pp.26-34, 0200.

J. Blanchard, F. Guillet, H. Briand, and R. Gras, Une version discriminante de l'indice probabiliste d'écart à l'équilibre pour mesurer la qualité des règles, Troisièmes rencontres internationales de l'Analyse Statistique Implicative (ASI 05), pp.131-137, 0200.

]. J. Bgbg5c, F. Blanchard, H. Guillet, R. Briand, and . Gras, Assessing rule interestingness with a probabilistic measure of deviation from equilibrium, Proceedings of the 11th international symposium on Applied Stochastic Models and Data Analysis ASMDA-2005, pp.191-200, 2005.

. Bgg-+-13-]-r, D. Belohlavek, S. Grissa, E. M. Guillaume, J. Nguifo et al., Boolean factors as a means of clustering interestingness measures of association rules, Annals of Mathematics and Artificial Intelligence (AMAI), vol.67, p.2013

F. [. Blanchard, R. Guillet, H. Gras, and . Briand, Mesurer la qualité des règles et de leurs contraposées avec le taux informationnel tic, Actes des quatrièmes journées Extraction et Gestion des Connaissances, volume RNTI-E-2 of Revue des Nouvelles Technologies de l'Information, pp.287-298, 2004.

H. [. Barber and . Hamilton, Extracting share frequent itemsets with infrequent subsets, Data Min. Knowl. Discov, vol.7, issue.2, pp.2003-2024

P. [. Blanchard, F. Kuntz, H. Guillet, and . Briand, Mesure de qualité des règles d'association par l'intensité d'implication entropique, Actes des 4èmes EGC' 04 E1 of Revue des Nouvelles Technologies de l'Information, Numéro spécial Mesures de qualité pour la fouille de données, pp.33-44, 2004.

P. [. Le-bras, S. Lenca, and . Lallich, Formal framework for the study of algorithmic properties of objective interestingness measures, volume 24 of Intelligent Systems Reference Library, Data Mining : Foundations and Intelligent Paradigms, chapter Data Mining : Foundations and Intelligent Paradigms, pp.77-98, 2012.

[. Barthélemy, A. Legrain, P. Lenca, and B. Vaillant, Aggregation of Valued Relations Applied to Association Rule Interestingness Measures, MDAI, pp.203-214, 2006.
DOI : 10.1007/978-3-540-30214-8_23

]. Y. Bmll10a, P. Le-bras, P. Meyer, S. Lenca, and . Lallich, Mesure de la robustesse de règles d'association, QDC 2010 : atelier Qualité des Données et des Connaissances, en conjonction avec Extraction et gestion des connaissances, pp.27-38, 2010.

]. Y. Bmll10b, P. Le-bras, P. Meyer, S. Lenca, and . Lallich, A robustness measure of association rules, ECML/PKDD, pp.227-242

R. [. Brin, C. Motwani, and . Silverstein, Beyond market baskets : Generalizing association rules to correlations, Proc. ACM SIGMOD Intl. Conf. Management of Data, pp.265-276, 1997.

. Bpt-+-00-]-y, N. Bastide, R. Pasquier, G. Taouil, L. Stumme et al., Mining minimal non-redundant association rules using frequent closed itemsets, Proc. of the First Intl. Conf. on Computational Logic, CL'00, pp.972-986, 2000.

]. Y. Bra11 and . Bras, Contribution à l'étude des mesures de l'intérêt des règles d'association et à leurs propriétés algorithmiques. Phd thèse, Université de Bretagne Sud (UBS), Lab-STICC UMR CNRS 3192, pp.167-167, 2011.

R. [. Bouker, S. B. Saidi, E. M. Yahia, and . Nguifo, Ranking and Selecting Association Rules Based on Dominance Relationship, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, pp.658-665
DOI : 10.1109/ICTAI.2012.94

URL : https://hal.archives-ouvertes.fr/hal-00677853

]. W. Bun96 and . Buntine, Graphical models for discovering knowledge, Advances in Knowledge Discovery and Data Mining, pp.59-82

V. [. Belohlavek and . Vychodil, Discovery of optimal factors in binary data via a novel method of matrix decomposition, Journal of Computer and System Sciences, vol.76, issue.1, pp.3-20, 2010.
DOI : 10.1016/j.jcss.2009.05.002

K. [. Brijs, G. Vanhoof, and . Wets, Defining interestigness for association rules, International Journal ITA, vol.10, issue.4, pp.370-375, 2003.

V. [. Chandra and . Anuradha, A Survey on Clustering Algorithms for Data in Spatial Database Management Systems, International Journal of Computer Applications, vol.24, issue.9, pp.19-26, 2011.
DOI : 10.5120/2969-3975

]. G. Cel89 and . Celeux, Classification automatique des données : environnement statistique et informatique . Dunod, 1989.

A. [. Carvalho, N. Freitas, and . Ebecken, Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest, Proc. of Principles of Data Mining and Knowledge Discovery, PKDD '05, pp.453-461, 2005.
DOI : 10.1007/11564126_45

P. [. Church and . Hanks, Word association norms, mutual information, and lexicography, Proceedings of the 27th annual meeting on Association for Computational Linguistics -, pp.22-29, 0200.
DOI : 10.3115/981623.981633

J. [. Chen, P. Han, and . Yu, Data mining : An overview from a database perspective

L. [. Cleuziou, C. Martin, and . Vrain, Poboc : An overlapping clustering algorithm, application to rule-based classification and textual data, Proc. of the 16th European Conf. on Artificial Intelligence (ECAI-04), pp.440-444, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00084986

E. [. Couturier and . Nguifo, Une approche anthropocentrée interactive pour l'aide à la décision en marketing bancaire of the Association Francophone d'Interaction Homme-Machine, IHM', Proc. of the 18th Intl. Conf of ACM International Conference Proceeding Series, pp.253-256, 2006.

]. J. Coh60 and . Cohen, A Coefficient of Agreement for Nominal Scales, Educational and Psychological Measurement, vol.20, issue.1, pp.37-46, 1960.

G. [. Carpineto and . Romano, GALOIS : An order-theoretic approach to conceptual clustering, Proceedings of the 10th International Conference on Machine Learning (ICML'90), pp.33-40, 1993.
DOI : 10.1016/B978-1-55860-307-3.50011-3

J. [. Ceglar and . Roddick, Association mining, ACM Computing Surveys, vol.38, issue.2, p.2006
DOI : 10.1145/1132956.1132958

J. [. Cheeseman and . Stutz, Advances in knowledge discovery and data mining. chapter Bayesian classification (AutoClass) : theory and results, American Association for Artificial Intelligence, pp.153-180, 1996.

Q. [. Chan, Y. Yang, and . Shen, Mining high utility itemsets, ICDM, pp.19-26, 2003.

]. J. Cze13 and . Czekanowski, Zarys metod statystycznych (die grundzuge der statischen metoden, p.49, 0199.

]. A. Czy96 and . Czyzewski, Mining knowledge in noisy audio data, Proc. of the 2nd ACM Intl. Conf. on Knowledge Discovery and Data Mining, KDD '96, pp.220-225, 1996.

]. E. Did71 and . Diday, Une nouvelle méthode en classification automatique et reconnaissance des formes : la méthode des nuées dynamiques, pp.19-33, 1971.

]. E. Did86 and . Diday, Orders and Overlapping Clusters by Pyramids, Multidimentional Data Analysis, 1986.

H. [. Diatta, A. Ralambondrainy, and . Totohasina, Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules, Quality Measures in Data Mining, pp.237-250, 2007.
DOI : 10.1007/978-3-540-44918-8_10

]. J. Dun73 and . Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, vol.3, issue.3, pp.32-57, 1973.

[. Ester, H. Kriegel, and X. Xu, A database interface for clustering in large spatial databases, Proc. ACM Intl. Conf. on Knowledge Discovery and Data Mining, KDD '95, pp.94-99, 1995.

]. D. Fen07 and . Feno, Mesures de qualité des règles d'association : normalisation et caractérisation des bases, pp.31-31, 2007.

Y. [. Fukuda, S. Morimoto, T. Morishita, and . Tokuyama, Data mining using two-dimensional optimized association rules : Scheme, algorithms, and visualization, Proc. ACM SIGMOD Intl. Conf. Management of Data, pp.13-23, 0200.

G. [. Frawley, C. J. Piatetsky-shapiro, and . Matheus, Knowledge discovery in databases : An overview, Knowledge Discovery in Databases, pp.1-30, 1991.

]. U. Fpss96, G. Fayyad, P. Piatetsky-shapiro, and . Smyth, From data mining to knowledge discovery in databases, pp.37-54, 1996.

M. U. Fayyad, G. Piatetsky-shapiro, P. Smyth, and R. Uthurusamy, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, vol.8, pp.8-9, 1996.

]. A. Fre99 and . Freitas, On rule interestingness measures. Knowledge-Based Systems, pp.309-315, 1999.

. Gab-+-96-]-r, S. Gras, . Ag, M. Almouloud, A. Bailleuil et al., Ratsimba-Rajohn, and A. Totohasina . L'implication statistique, nouvelle méthode exploratoire de données, p.53, 1996.

D. [. Gavrilov, P. Anguelov, R. Indyk, and . Motwani, Mining the stock market (extended abstract), Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.487-496, 2000.
DOI : 10.1145/347090.347189

]. J. Gan87 and . Ganascia, Charade:Arulesystem learning system, Proc. of the tenth International Jointed Conference in Artificial Intelligence (IJCAI, pp.345-347, 1987.

R. Gras, R. Couturier, J. Blanchard, H. Briand, P. Kuntz et al., Mesures de qualité pour la fouille de données, chapter Quelques critères pour une mesure de qualité de règles d'association. Un exemple : l'implication statistique, Cépaduès éditions, pp.3-32, 2004.

. S. Ggm, D. Guillaume, E. M. Grissa, and . Nguifo, Propriétés des mesures d'intérêt pour l'extraction des règles, pp.31-2009

D. [. Guillaume, E. M. Grissa, and . Nguifo, Propriétés des mesures d'intérêt pour l'extraction des règles, Actes de l'atelier QDC de la conférence EGC, pp.15-28

H. [. Geng and . Hamilton, Choosing the right lens : Finding what is interesting in data mining Quality Measures in Data Mining, Studies in Computational Intelligence, vol.43, pp.3-24, 2007.

W. [. Goodman, Measures of association for cross classifications, J. Am. Stat. Assoc, vol.49, pp.732-764, 0200.

P. [. Gras, R. Kuntz, F. Couturier, and . Guillet, Une version entropique de l'intensité d'implication pour les corpus volumineux, EGC, volume 1 of Extraction des Connaissances et Apprentissage, pp.69-80, 2001.

]. I. Goo65 and . Good, The estimation of probabilities : An essay on modern bayesian methods, 1965.

]. R. Gra79 and . Gras, Contribution à l'étude expérimentale et à l'analyse de certaines acquisitions cognitives et de certains objectifs didactiques en mathématiques, pp.53-67, 1979.

P. [. Goodman and . Smyth, THE INDUCTION OF PROBABILISTIC RULE SETS??? THE ITRULE ALGORITHM
DOI : 10.1016/B978-1-55860-036-2.50040-0

C. [. Piatetsky-shapiro and . Matheus, The interestingness of deviations, Knowledge Discovery and Data Mining Workshop, KDD '94, pp.25-36, 1994.

]. S. Gué06 and . Guérif, Réduction de dimension en apprentissage numérique non supervisé, 2006.

]. S. Gui00 and . Guillaume, Traitement des données volumineuses Mesures et algorithmes d'extraction des règles d'association et règles ordinales, pp.25-49, 2000.

R. [. Ganter and . Wille, Formal Concept Analysis : Mathematical Foundations, 1997.

M. [. Gouda and . Zaki, Efficiently mining maximal frequent itemsets, Proceedings 2001 IEEE International Conference on Data Mining, pp.163-170, 2001.
DOI : 10.1109/ICDM.2001.989514

]. J. Har75 and . Hartigan, Clustering Algorithms, 1975.

B. [. Hébert and . Crémilleux, Optimized Rule Mining Through a Unified Framework for Interestingness Measures, Proc. of the 8th Intl. Conf. on Data Warehousing and Knowledge Discovery, DaWaK '06, pp.238-247, 2006.
DOI : 10.1007/11823728_23

B. [. Hébert and . Crémilleux, A Unified View of Objective Interestingness Measures, 5th International Conference on Machine Learning and Data Mining (MLDM'07), pp.533-547, 2007.
DOI : 10.1007/978-3-540-73499-4_40

]. Hgb05a, F. Huynh, H. Guillet, and . Briand, Clustering interestingness measures with positive correlation, Proceedings ICEIS (2), pp.248-253, 2005.

]. Hgb05b, F. Huynh, H. Guillet, and . Briand, A data analysis approach for evaluating the behavior of interestingness measures, Proc. of the 8th Intl. Conf. on Discovery Science, DS '05, pp.330-337, 0192.

[. Huynh, F. Guillet, and H. Briand, Arqat : plateforme exploratoire pour la qualité des règles d'association. Revue des Nouvelles Technologies de l'Information (EGC : Etat et perspectives), RNTI-E-5, 2006.

F. Huynh, J. Guillet, P. Blanchard, H. Kuntz, R. Briand et al., A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study, Quality Measures in Data Mining, volume 43 of Studies in Computational Intelligence, pp.25-50, 2007.
DOI : 10.1007/978-3-540-44918-8_2

URL : https://hal.archives-ouvertes.fr/hal-00420991

U. [. Hipp, G. Güntzer, and . Nakhaeizadeh, Algorithms for association rule mining --- a general survey and comparison, ACM SIGKDD Explorations Newsletter, vol.2, issue.1, pp.58-64, 2000.
DOI : 10.1145/360402.360421

H. [. Hilderman and . Hamilton, Applying Objective Interestingness Measures in Data Mining Systems, Proc. of the 4th European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD '00, pp.432-439, 2000.
DOI : 10.1007/3-540-45372-5_47

H. [. Hilderman and . Hamilton, Knowledge Discovery and Measures of Interest, volume 638 of The Springer International Series in Engineering and Computer Science, pp.21-148, 2001.

. Hkm-+-96-]-k, M. Hätönen, H. Klemettinen, P. Mannila, H. Ronkainen et al., Knowledge discovery from telecommunication network alarm databases, ICDE, pp.115-122, 1996.

H. [. Hussain, E. Liu, H. Suzuki, and . Lu, Exception Rule Mining with a Relative Interestingness Measure, Proc. of Pacific Asia Conference on Knowledge Discovery in DataBases, PAKDD '00, pp.86-97, 2000.
DOI : 10.1007/3-540-45571-X_11

H. [. Hand, P. Mannila, and . Smyth, Principles of Data Mining, Drug Safety, vol.15, issue.2, 2001.
DOI : 10.2165/00002018-200730070-00010

]. R. Hue09 and . Huebner, Diversity-based interestingness measures for association rule mining, Proc. of ASBBS, 2009.

]. Huy06 and . Huynh, Interestingness Measures for Association Rules in a KDD Process : Postprocessing of Rules with ARQAT Tool, pp.31-31, 2006.

A. [. Hofmann and . Wilhelm, Visual Comparison of Association Rules, Computational Statistics, vol.16, issue.3, pp.399-415, 2001.
DOI : 10.1007/s001800100075

T. [. Hacène and . Yannick, Fouille de textes par combinaison de règles d'association et d'indices statistiques, 1er Colloque International sur la Fouille de Textes -CIFT'2002, 2002.

. Hyn-+-12-]-l, H. Haibing, S. W. Yuan, W. Nick, T. Fei et al., Overlapping clustering with sparseness constraints, 12th IEEE International Conference on Data Mining Workshops, ICDM Workshops, pp.486-494

R. [. Heravi and . Zaïane, A study on interestingness measures for associative classifiers

R. [. Bayardo-jr and . Agrawal, Mining the most interesting rules, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.145-154, 1999.
DOI : 10.1145/312129.312219

]. P. Jac08 and . Jaccard, Nouvelles recherches sur la distribution florale, Bulletin de la Société Vaudense des Sciences Naturelles., number 44, pp.223-270, 1908.

F. [. Jimãl-'nez, J. Berzal, and . Cubero, Interestingness measures for association rules within groups, Journal Intelligent Data Analysis, vol.17, issue.2, pp.195-215, 2013.

R. [. Jain and . Dubes, Algorithms for clustering data, 1988.

]. H. Jef35 and . Jeffreys, Some tests of significance treated by the theory of probability, Proc. of the Cambridge Philosophical Society, pp.203-222, 0200.

R. [. Joshi and . Kaur, A review : Comparative study of various clustering techniques in data mining, International Journal of Advanced Research in Computer Science and Software Engineering, vol.3, issue.3, p.2013

M. [. Jain, P. J. Murty, and . Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.264-323, 1999.
DOI : 10.1145/331499.331504

D. [. Jaroszewicz and . Simovici, A General Measure of Rule Interestingness, Proc. of Principles of Data Mining and Knowledge Discovery, PKDD '01, pp.253-265, 2001.
DOI : 10.1007/3-540-44794-6_21

G. [. Kuramochi and . Karypis, Finding frequent patterns in a large sparse graph, SDM, 2004.

D. [. Kotsiantis and . Kanellopoulos, Association rules mining : A recent overview, GESTS International Transactions on Computer Science and Engineering, vol.32, issue.1, pp.71-82, 2006.

]. W. Klo96 and . Klosgen, Advances in knowledge discovery and data mining. chapter Explora : a multipattern and multistrategy discovery assistant, pp.249-271

M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo, Finding interesting rules from large sets of discovered association rules, Proceedings of the third international conference on Information and knowledge management , CIKM '94, pp.401-407, 1994.
DOI : 10.1145/191246.191314

H. [. Klemettinen, H. Mannila, and . Toivonen, A data mining methodology and its application to semi-automatic knowledge acquisition, Database and Expert Systems Applications. 8th International Conference, DEXA '97. Proceedings, pp.670-677, 1997.
DOI : 10.1109/DEXA.1997.617410

]. Y. Kod99 and . Kodratoff, Quelques contraintes symboliques sur le numérique en ecd et ect, SFDS, pp.183-188, 1999.

]. Y. Kod01 and . Kodratoff, Comparing machine learning and knowledge discovery in databases : An application to knowledge discovery in texts, Machine Learning and Its Applications, pp.1-21, 2001.

]. I. Kon95 and . Kononenko, On biases in estimating multi-valued attributes, Proceedings of the 14th international joint conference on Artificial intelligence, pp.1034-1040, 1995.

P. [. Kaufman and . Rousseeuw, Finding Groups in Data : An Introduction to Cluster Analysis, pp.78-79, 1990.
DOI : 10.1002/9780470316801

R. [. Kamber and . Shinghal, Proposed interestingness measure for characteristic rules, AAAI/IAAI, pp.1393-1393, 1996.

M. [. Kohonen, T. S. Schroeder, and . Huang, Self-Organizing Maps, 2001.

]. S. Kul28 and . Kulczynski, Die p anzenassoziationen der pieninen, pp.57-203, 1928.

[. Lerman and J. Azé, A New Probabilistic Measure of Interestingness for Association Rules, Based on the Likelihood of the Link, Studies in Computational Intelligence, vol.43, pp.207-236, 2007.
DOI : 10.1007/978-3-540-44918-8_9

URL : https://hal.archives-ouvertes.fr/inria-00180117

]. S. Lal02 and . Lallich, Mesure et validation en extraction des connaissances à partir des données. Habilitation à diriger des recherches, pp.58-67, 2002.

]. A. Lel93 and . Lelu, Modèles neuronaux pour l'analyse de données documentaires et textuelles, 1993.

]. Ler70 and . Lerman, Sur l'analyse des données préalable à une classification automatique (proposition d'une nouvelle mesure de similarité) Mathématiques et sciences humaines, pp.5-15, 1970.

P. [. Lavrac, B. Flach, and . Zupan, Rule Evaluation Measures: A Unifying View, Proc. of the 9th Intl. Workshop on Inductive Logic Programming, ILP '99, pp.174-185, 0200.
DOI : 10.1007/3-540-48751-4_17

R. [. Lerman, H. Gras, and . Rostam, Élaboration et évaluation d'un indice d'implication pour des données binaires. i, Mathématiques et Sciences Humaines, vol.74, pp.5-35, 0200.

W. [. Liu and . Hsu, Post-analysis of learned rules, AAAI/IAAI, pp.828-834, 1996.

W. [. Liu, S. Hsu, Y. Chen, and . Ma, Analyzing the subjective interestingness of association rules, IEEE Intelligent Systems, vol.15, issue.5, pp.47-55, 2000.

B. De-la-iglesia, J. C. Debuse, and V. J. Rayward-smith, Discovering knowledge in commercial databases using modern heuristic techniques, Proc. of the ACM Intl. Conf. on Knowledge Discovery and Data Mining, KDD '96, pp.44-49, 1996.

H. [. Liu and . Lu, Mining weak rules, In COMPSAC, pp.309-310, 1999.

P. Lenca, P. Meyer, P. Picouet, B. Vaillant, and S. Lallich, Critères d'évaluation des mesures de qualité en ECD, pp.123-134, 2003.

P. [. Lenca, P. Meyer, B. Picouet, and . Vaillant, Aide multicritère à la décision pour évaluer les indices de qualité des connaissances, EGC, pp.271-282, 2003.

P. [. Lenca, B. Meyer, S. Vaillant, and . Lallich, A multicriteria decision aid for interestingness measure selection, pp.22-29, 2004.

P. [. Lenca, B. Meyer, S. Vaillant, and . Lallich, On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid, European Journal of Operational Research, vol.184, issue.2, pp.610-626, 2008.
DOI : 10.1016/j.ejor.2006.10.059

]. J. Loe47 and . Loevinger, A systematic approach to the construction and evaluation of tests of ability, Psychological monographs, p.53, 0200.

J. [. Liu and . Qu, Mining high utility itemsets without candidate generation, Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM '12, pp.55-64
DOI : 10.1145/2396761.2396773

[. Lesot and M. Rifqi, Order-Based Equivalence Degrees for Similarity and Distance Measures, IPMU'10, pp.19-28, 2010.
DOI : 10.1007/978-3-642-14049-5_3

URL : https://hal.archives-ouvertes.fr/hal-01073168

O. [. Lallich and . Teytaud, Évaluation et validation de mesures d'intérêt des règles d'association . Revue des Nouvelles Technologies de l'Information., RNTI-E-1. Cépaduès, pp.193-217, 2004.

B. [. Lallich, P. Vaillant, and . Lenca, Parametrised measures for the evaluation of association rule interestingness, The XIth Intl. Symp. on Applied Stochastic Models and Data Analysis, pp.220-229, 2005.

B. [. Lenca, P. Vaillant, S. Meyer, and . Lallich, Association Rule Interestingness Measures: Experimental and Theoretical Studies, Quality Measures in Data Mining, pp.51-76, 2007.
DOI : 10.1007/978-3-540-44918-8_3

URL : https://hal.archives-ouvertes.fr/hal-01373475

]. J. Mac67 and . Macqueen, Some methods for classification and analysis of multivariate observations, Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

]. K. Mcg05 and . Mcgarry, A survey of interestingness measures for knowledge discovery, Knowledge Eng. Review, vol.20, issue.1 2 21, pp.39-61, 2005.

]. Y. Mfm-+-98, T. Morimoto, H. Fukuda, T. Matsuzawa, K. Tokuyama et al., Algorithms for mining association rules for binary segmentations of huge categorical databases, Prod. of the 24rd International Conference on Very Large Data Bases, VLDB '98, pp.380-391, 1998.

J. [. Maddouri and . Gammoudi, On Semantic Properties of Interestingness Measures for Extracting Rules from Data, Proc of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I, ICANNGA '07, pp.148-158, 2007.
DOI : 10.1007/978-3-540-71618-1_17

J. [. Mcgarry and . Malone, Analysis of Rules Discovered by the Data Mining Process, Applications and Science in Soft Computing Series : Advances in Soft Computing, pp.219-224, 2004.
DOI : 10.1007/978-3-540-45240-9_30

H. [. Mannila, A. I. Toivonen, and . Verkamo, Efficient algorithms for discovering association rules, Knowledge Discovery and Data Mining Workshop, KDD '94, pp.181-192, 1994.

M. Mcleach, P. Yao, M. Garg, and T. Stirtzinger, Discovery of medical diagnostic information : An overview of methods and results, Knowledge Discovery in Databases, pp.477-490, 1991.

]. A. Och57 and . Ochiai, Zoogeographic studies on the soleoid shes found in japan and its neighbouring regions, bull, pp.526-530, 1957.

M. Ohsaki, S. Kitaguchi, H. Yokoi, and T. Yamaguchi, Investigation of Rule Interestingness in Medical Data Mining, Active Mining, pp.174-189, 2003.
DOI : 10.1007/11423270_10

E. [. Ordonez and . Omiecinski, Image mining : A new approach for data mining, Georgia Institute of Technology, 1998.

M. Ohsaki, Y. Sato, S. Kitaguchi, H. Yokoi, and T. Yamaguchi, Comparison between Objective Interestingness Measures and Real Human Interest in Medical Data Mining, Proc. of the 17th Intl. Conf. on Innovations in Applied Artificial Intelligence, pp.1072-1081, 2004.
DOI : 10.1007/978-3-540-24677-0_110

]. B. Pat10 and . Patrice, Classifications en classes recouvrantes ou non, et leurs dissimilarités, Mathématiques et Sciences Humaines, vol.2, issue.190, pp.59-87

Y. [. Pasquier, R. Bastide, L. Taouil, and . Lakhal, Discovering Frequent Closed Itemsets for Association Rules, Proc. of the 7th ICDT'99, pp.398-416, 1999.
DOI : 10.1007/3-540-49257-7_25

URL : https://hal.archives-ouvertes.fr/hal-00467747

]. K. Pea96 and . Pearson, Mathematical contributions to the theory of evolution. iii. regression, heredity and panmixia, Philosophical Transactions of The Royal Society A : Mathematical, Physical and Engineering Sciences, vol.187, pp.253-318

]. K. Pea01 and . Pearson, On lines and planes of closest fit to systems of points in space, Philosophical Magazine, vol.2, issue.6, pp.559-572, 1901.

]. J. Pea88 and . Pearl, Probabilistic reasoning in intelligent systems : networks of plausible inference

M. Plasse, N. Niang-keita, G. Saporta, and L. Leblond, Une comparaison de certains indices de pertinence des règles d'association, EGC, volume RNTI-E-6 of Revue des Nouvelles Technologies de l'Information, pp.561-568, 2006.

S. [. Pazzani, W. R. Mani, and . Shankle, Comprehensible knowledge-discovery in databases, Proc.of the 19th Intl. Conf. of the Cognitive Science society (COGSCI'97), pp.596-601, 1997.

]. G. Ps91a and . Piatetsky-shapiro, Discovery, analysis and presentation of strong rules, Knowledge Discovery in Databases, pp.229-248, 1991.

]. G. Ps91b and . Piatetsky-shapiro, Knowledge discovery in real databases : A report on the ijcai-89 workshop. AI Magazine, pp.68-70, 1991.

]. B. Pt98a, A. Padmanabhan, and . Tuzhilin, A belief-driven method for discovering unexpected patterns, ACM Intl. Conf. on Knowledge Discovery and Data Mining, pp.94-100, 1998.

]. B. Pt98b, A. Padmanabhan, and . Tuzhilin, A belief-driven method for discovering unexpected patterns, ACM Intl. Conf. on Knowledge Discovery and Data Mining, KDD '98, pp.94-100, 1998.

W. Romão, A. A. Freitas, and I. M. De-souza-gimenes, Discovering interesting knowledge from a science and technology database with a genetic algorithm, Applied Soft Computing, vol.4, issue.2, pp.121-137, 2004.
DOI : 10.1016/j.asoc.2003.10.002

A. [. Rakotomalala and . Morineau, The TVpercent principle for the counterexamples statistic , volume 127 of Statistical Implicative Analysis : theory and applications, pp.449-462

]. S. Sah99 and . Sahar, Interestingness via what is not interesting, Proc. of the 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp.332-336, 1999.

A. [. Segal, D. Battle, and . Koller, DECOMPOSING GENE EXPRESSION INTO CELLULAR PROCESSES, Biocomputing 2003, pp.89-100, 2003.
DOI : 10.1142/9789812776303_0009

R. [. Smyth and . Goodman, Rule induction using information theory, In Knowledge Discovery in Databases KDD, issue.2, pp.159-176, 1991.

U. [. Surana, P. K. Kiran, and . Reddy, Selecting a right interestingness measure for rare association rules, Proc. of the 16th Intl. Conf. on Management of Data, pp.115-124, 2010.

S. [. Sese and . Morishita, Answering the Most Correlated N Association Rules Efficiently, Proc. of Principles of Data Mining and Knowledge Discovery, PKDD '02, pp.410-422
DOI : 10.1007/3-540-45681-3_34

M. [. Sebag and . Schoenauer, Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases, Proc. of the European Knowledge Acquisition Workshop (EKAW'88), pp.54-202, 1988.

A. [. Silberschatz and . Tuzhilin, On subjective measures of interestingness in knowledge discovery, Proc. of the ACM Intl. Conf. on Knowledge Discovery and Data Mining, KDD '95, pp.275-281, 1995.

A. [. Silberschatz and . Tuzhilin, What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and Data Engineering, vol.8, issue.6, pp.970-974, 1996.
DOI : 10.1109/69.553165

]. E. Suz08 and . Suzuki, Pitfalls for categorizations of objective interestingness measures for rule discovery, Statistical Implicative Analysis, pp.383-395, 2008.

]. E. Suz09a and . Suzuki, Compression-based measures for mining interesting rules, Proc. of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems : Next-Generation Applied Intelligence, IEA/AIE '09, pp.741-746, 2009.

]. E. Suz09b and . Suzuki, Negative encoding length as a subjective interestingness measure for groups of rules, Proc. of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD '09, pp.220-231, 2009.

H. [. Shoshani and . Wong, Statistical and Scientific Database Issues, IEEE Transactions on Software Engineering, vol.11, issue.10, pp.1040-1047, 1985.
DOI : 10.1109/TSE.1985.231851

C. Tew, C. Giraud-carrier, K. Tanner, and S. Burton, Behavior-based clustering and analysis of interestingness measures for association rule mining, Data Mining and Knowledge Discovery, vol.75, issue.6, pp.1-2, 2013.
DOI : 10.1007/s10618-013-0326-x

V. [. Tan, J. Kumar, and . Srivastava, Selecting the right interestingness measure for association patterns, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.32-41, 2002.
DOI : 10.1145/775047.775053

V. [. Tan, J. Kumar, and . Srivastava, Selecting the right objective measure for association analysis, Information Systems, vol.29, issue.4, pp.293-313, 2004.
DOI : 10.1016/S0306-4379(03)00072-3

Y. [. Tamir and . Singer, On a confidence gain measure for association rule discovery and scoring. Very Large Data Bases, pp.40-52, 2006.

H. [. Tsumoto and . Tanaka, Automated discovery of functional components of proteins from amino-acid sequences based on rough sets and change of representation, Proc. of the ACM Intl. Conf. on Knowledge Discovery and Data Mining, KDD '95, pp.318-324, 1995.

]. S. Tuf05 and . Tufféry, Data Mining et statistique décisionnelle : L'intelligence dans les bases de données, pp.80-209, 2005.

]. B. Vai02 and . Vaillant, Évaluation de connaissances : le problème du choix d'une mesure de qualité en extraction de connaissances à partir des données. Master's thesis, École Nationale Supérieure des Télécommunications de Bretagne, 2002.

]. B. Vai06 and . Vaillant, Mesurer la qualité des règles d'association : études formelles et expérimentales, pp.121-124, 2006.

P. [. Vaillant, S. Lenca, and . Lallich, Étude expérimentale de mesures de qualités de règles d'association, Actes des 4èmes EGC' 04, pp.341-352, 2004.

S. [. Vaillant, P. Lallich, and . Lenca, Modeling of the counter-examples and association rules interestingness measures behavior, DMIN, pp.132-137, 2006.

]. J. War63 and . Ward, Hierarchical grouping to optimize an objective function, Journal of the American statistical association, vol.58, issue.301, pp.236-244, 1963.

E. [. Witten and . Frank, Data mining, ACM SIGMOD Record, vol.31, issue.1, 2000.
DOI : 10.1145/507338.507355

V. Wu, J. R. Kumar, J. Quinlan, Q. Ghosh, H. Yang et al., Top 10 algorithms in data mining, Knowledge and Information Systems, vol.9, issue.2, pp.1-37, 2007.
DOI : 10.1007/s10115-007-0114-2

H. Xiong, X. He, C. H. Ding, Y. Zhang, V. Kumar et al., IDENTIFICATION OF FUNCTIONAL MODULES IN PROTEIN COMPLEXES VIA HYPERCLIQUE PATTERN DISCOVERY, Biocomputing 2005, 2005.
DOI : 10.1142/9789812702456_0021

Y. [. Xu and . Li, Generating concise association rules, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management , CIKM '07, pp.781-790, 2007.
DOI : 10.1145/1321440.1321549

H. [. Yao and . Hamilton, Mining itemset utilities from transaction databases, Data & Knowledge Engineering, vol.59, issue.3, pp.603-626, 2006.
DOI : 10.1016/j.datak.2005.10.004

]. G. Yul12 and . Yule, On the Methods of Measuring Association Between Two Attributes, Journal of the Royal Statistical Society, vol.75, issue.6, pp.579-652, 1912.

]. G. Yul27 and . Yule, On a method of investigating periodicities in disturbed series, with special reference to Wolfer's sunspot numbers, Containing papers of a Mathematical or Physical Character, pp.267-298, 1927.

N. [. Yao and . Zhong, An Analysis of Quantitative Measures Associated with Rules, Proc. of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, PAKDD '99, pp.479-488, 1999.
DOI : 10.1007/3-540-48912-6_64

R. [. Zighed, A. Abdesselam, and . Bounekkar, Équivalence topologique entre mesures de proximité, EGC, volume RNTI-E-20 of Revue des Nouvelles Technologies de l'Information, pp.53-64, 2011.

]. M. Zak00 and . Zaki, Generating non-redundant association rules, ACM Intl. Conf. on Knowledge Discovery and Data Mining, pp.34-43, 2000.

]. T. Zha00 and . Zhang, Association rules, Proc. 4th Pacific-Asia Conference Knowledge Discovery and Data Mining, pp.21-203, 2000.

. R. Zhl-+-98-]-o, J. Zaïane, . Han, -. N. Ze, S. Li et al., Multimediaminer : A system prototype for multimedia data mining, Proc. ACM SIGMOD Intl. Conf. Management of Data, pp.581-583, 1998.

G. [. Zhao and . Karypis, Criterion Functions for Document Clustering : Experiments and Analysis, 2001.

R. [. Zhang, M. Ramakrishnan, and . Livny, Birch : An efficient data clustering method for very large databases, Proc. ACM SIGMOD Intl. Conf. Management of Data, SIGMOD '96, pp.103-114, 1996.

D. [. Zaman-ashrafi, K. A. Taniar, and . Smith, A new approach of eliminating redundant association rules, DEXA, pp.465-474, 2004.