Association Rule Interestingness Measures: Experimental and Theoretical Studies | SpringerLink
Skip to main content
Springer Nature Link
Log in

Association Rule Interestingness Measures: Experimental and Theoretical Studies

  • Chapter
Quality Measures in Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 43))

  • 1258 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 17159
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 21449
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
JPY 21449
Price includes VAT (Japan)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. H. Abe, S. Tsumoto, M. Ohsaki, and T. Yamaguchi. Evaluating model construction methods with objective rule evaluation indices to support human experts. In V. Torra, Y. Narukawa, A. Valls, and J. Domingo-Ferrer, editors, Modeling Decisions for Artificial Intelligence, volume 3885 of Lecture Notes in Computer Science, pages 93-104, Tarragona, Spain, 2006. Springer-Verlag.

    Google Scholar 

  2. R. Agrawal, T. Imielinski, and A.N. Swami. Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, ACM SIGMOD International Conference on Management of Data, pages 207-216, 1993.

    Google Scholar 

  3. J. Azé and Y. Kodratoff. Evaluation de la résistance au bruit de quelques mesures d’extraction de règles d’assocation. In D. Hérin and D.A. Zighed, editors, Extraction des connaissances et apprentissage, volume 1, pages 143-154. Hermes, 2002.

    Google Scholar 

  4. J. Azé and Y. Kodratoff. A study of the effect of noisy data in rule extraction systems. In The Sixteenth European Meeting on Cybernetics and Systems Research, volume 2, pages 781-786, 2002.

    Google Scholar 

  5. J. P. Barthélemy, A. Legrain, P. Lenca, and B. Vaillant. Aggregation of valued relations applied to association rule interestingness measures. In V. Torra, Y. Narukawa, A. Valls, and J. Domingo-Ferrer, editors, Modeling Decisions for Artificial Intelligence, volume 3885 of Lecture Notes in Computer Science, pages 203-214, Tarrogona, Spain, 2006. Springer-Verlag.

    Google Scholar 

  6. R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 145-154, 1999.

    Google Scholar 

  7. R. Bisdorff. Bipolar ranking from pairwise fuzzy outrankings. Belgian Journal of Operations Research, Statistics and Computer Science, 37(4) :379-387, 1999.

    Google Scholar 

  8. C.L. Blake and C.J. Merz. UCI repository of machine learning databases. http://www.ics.uci.edu/∼mlearn/MLRepository.html, 1998.

  9. J. Blanchard, F. Guillet, and H. Briand. A virtual reality environment for knowledge mining. In R. Bisdorff, editor, Human Centered Processes, pages 175-179, Luxembourg, 2003.

    Google Scholar 

  10. J. Blanchard, F. Guillet, H. Briand, and R. Gras. Assessing the interestingness of rules with a probabilistic measure of deviation from equilibrium. In J. Janssen and P. Lenca, editors, The XIth International Symposium on Applied Stochastic Models and Data Analysis, pages 191-200, Brest, France, 2005.

    Google Scholar 

  11. J. Blanchard, F. Guillet, H. Briand, and R. Gras. IPEE : Indice probabiliste d’écart à l’équilibre pour l’évaluation de la qualité des règles. In Atelier Qualité des Données et des Connaissances (EGC 2005), pages 26-34, 2005.

    Google Scholar 

  12. J. Blanchard, F. Guillet, R. Gras, and H. Briand. Using information-theoretic measures to assess association rule interestingness. In The 5th IEEE International Conference on Data Mining, pages 66-73, Houston, Texas, USA, 2005. IEEE Computer Society Press.

    Google Scholar 

  13. C. Borgelt and R. Kruse. Induction of association rules: Apriori implementation. In Compstat’02, pages 395-400, Berlin, Germany, 2002. Physica Verlag.

    Google Scholar 

  14. J.P. Brans and B. Mareschal. promethee-gaia - Une méthode d’aide à la décision en présence de critères multiples. Ellipses, 2002.

    Google Scholar 

  15. J.P. Brans and P. Vincke. A preference ranking organization method. Manage- ment Science, 31(6):647-656, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  16. T. Brijs, K. Vanhoof, and G. Wets. Defining interestingness for association rules. International journal of information theories and applications, 10(4):370-376,2003.

    Google Scholar 

  17. S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: generalizing association rules to correlations. In ACM SIGMOD/PODS’97 Joint Conference, pages 265-276, 1997.

    Google Scholar 

  18. S. Brin, R. Motwani, J.D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In J. Peckham, editor, ACM SIGMOD International Conference on Management of Data, pages 255-264, Tucson, Arizona, USA, 1997. ACM Press.

    Google Scholar 

  19. J.-H. Chauchat and A. Risson. Visualization of Categorical Data, chapter 3, pages 37-45. Blasius J. & Greenacre M. ed., 1998. New York: Academic Press.

    Google Scholar 

  20. K.W. Church and P. Hanks. Word association norms, mutual information an lexicography. Computational Linguistics, 16(1):22-29, 1990.

    Google Scholar 

  21. E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, J. Ullman, and C. Yang. Finding interesting associations without support pruning. In The 16th International conference on Data engineering, 2000.

    Google Scholar 

  22. J. Cohen. A coefficient of agreement for nominal scale. Educational and Psychological Measurement, 20:37-46, 1960.

    Article  Google Scholar 

  23. A.W.F. Edwards. The measure of association in a 2 x 2 table. Journal of the Royal Statistical Society, Series A, 126(1):109-114, 1963.

    Article  Google Scholar 

  24. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.

    Google Scholar 

  25. D. Feno, J. Diatta, and A. Totohasina. Normalisée d’une mesure probabiliste de la qualité des règles d’association : étude de cas. In Atelier Qualité des Données et des Connaissances (EGC 2006), pages 25-30, 2006.

    Google Scholar 

  26. A. Freitas. On rule interestingness measures. Knowledge-Based Systems journal, pages 309-315, 1999.

    Google Scholar 

  27. V. Giakoumakis and B. Monjardet. Coefficients d’accord entre deux préordres totaux. Statistique et Analyse des Données, 12(1 et 2):46-99, 1987.

    MathSciNet  Google Scholar 

  28. I.J. Good. The estimation of probabilities: An essay on modern bayesian methods. The MIT Press, Cambridge, MA, 1965.

    MATH  Google Scholar 

  29. R. Gras, S. Ag. Almouloud, M. Bailleuil, A. Larher, M. Polo, H. Ratsimba-Rajohn, and A. Totohasina. L’implication Statistique, Nouvelle Méthode Exploratoire de Données. Application à la Didactique, Travaux et Thèses. La Pensée Sauvage, 1996.

    Google Scholar 

  30. R. Gras, R. Couturier, J. Blanchard, H. Briand, P. Kuntz, and P. Peter. Quelques critères pour une mesure de qualité de règles d’association - un exemple: l’intensité d’implication. Revue des Nouvelles Technologies de l’Information (Mesures de Qualité pour la Fouille de Données), (RNTI-E-1):3-31, 2004.

    Google Scholar 

  31. R. Gras, P. Kuntz, R. Couturier, and F. Guillet. Une version entropique de l’intensité d’implication pour les corpus volumineux. In H. Briand and F. Guillet, editors, Extraction des connaissances et apprentissage, volume 1, pages 69-80. Hermes, 2001.

    Google Scholar 

  32. S. Greco, Z. Pawlak, and R. Slowinski. Can bayesian confirmation measures be useful for rough set decision rules? Engineering Applications of Artificial Intelligence, 17(4):345-361, 2004.

    Article  Google Scholar 

  33. S. Guillaume. Traitement des données volumineuses, Mesures et algorithmes d’extraction de règles d’association et règles ordinales. PhD thesis, Université de Nantes, 2000.

    Google Scholar 

  34. F. Guillet. Mesures de la qualité des connaissances en ECD. Atelier, Extraction et gestion des connaissances, 2004.

    Google Scholar 

  35. P. Hajek, I. Havel, and M. Chytil. The guha method of automatic hypotheses determination. Computing, (1):293-308, 1966.

    Google Scholar 

  36. R.J. Hilderman and H.J. Hamilton. Applying objective interestingness measures in data mining systems. In Fourth European Symposium on Principles of Data Mining and Knowledge Discovery, pages 432-439. Springer Verlag, 2000.

    Google Scholar 

  37. R.J. Hilderman and H.J. Hamilton. Evaluation of interestingness measures for ranking discovered knowledge. Lecture Notes in Computer Science, 2035:247-259,2001.

    Article  Google Scholar 

  38. R.J. Hilderman and H.J. Hamilton. Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, 2001.

    Google Scholar 

  39. R.J. Hilderman and H.J. Hamilton. Measuring the interestingness of discovered knowledge: A principled approach. Intelligent Data Analysis, 7(4):347-382,2003.

    MATH  Google Scholar 

  40. Y. Huang, H. Xiong, S. Shekhar, and J. Pei. Mining confident co-location rules without a support threshold. In The 18th Annual ACM Symposium on Applied Computing. ACM, 2003.

    Google Scholar 

  41. F. Hussain, H. Liu, E. Suzuki, and H. Lu. Exception rule mining with a relative interestingness measure. In T. Terano, H. Liu, and A.L.P. Chen, editors, The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining, volume 1805 of Lecture Notes in Artificial Intelligence, pages 86-97. SpringerVerlag, 2000.

    Google Scholar 

  42. X-H. Huynh, F. Guillet, and H. Briand. ARQAT: An exploratory analysis tool for interestingness measures. In J. Janssen and P. Lenca, editors, The XIth International Symposium on Applied Stochastic Models and Data Analysis, pages 334-344, Brest, France, 2005.

    Google Scholar 

  43. A. Iodice D’Enza, F. Palumbo, and M. Greenacre. Exploratory data analysis leading towards the most interesting binary association rules. In J. Janssen and P. Lenca, editors, The XIth International Symposium on Applied Stochastic Models and Data Analysis, pages 256-265, Brest, France, 2005.

    Google Scholar 

  44. S. Jaroszewicz and D.A. Simovici. A general measure of rule interestingness. In The 5th European Conference on Principles of Data Mining and Knowledge Discovery, pages 253-265, London, UK, 2001. Springer-Verlag.

    Google Scholar 

  45. H.J. Jeffreys. Some tests of significance treated by the theory of probability. In Proceedings of the Cambridge Philosophical Society, number 31, pages 203-222, 1935.

    Google Scholar 

  46. M. Kamber and R. Shingal. Evaluating the interestingness of characteristic rules. In The Second International Conference on Knowledge Discovery and Data Mining, pages 263-266, Portland, Oregon, August 1996.

    Google Scholar 

  47. D. A. Keim. Information visualization and visual data mining. IEEE Transactions On Visualization And Computer Graphics, 7(1):100-107, 2002.

    MathSciNet  Google Scholar 

  48. M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. In N.R. Adam, B.K. Bhargava, and Y. Yesha, editors, Third International Conference on Information and Knowledge Management, pages 401-407. ACM Press, 1994.

    Google Scholar 

  49. S. Lallich. Mesure et validation en extraction des connaissances à partir des données. Habilitation à Diriger des Recherches - Université Lyon 2, 2002.

    Google Scholar 

  50. S. Lallich, E. Prudhomme, and O. Teytaud. Contrôle du risque multiple en sélection de règles d’association significatives. In G. Hébrail, L. Lebart, and J.-M. Petit, editors, Extraction et gestion des connaissances, volume 1-2, pages 305-316. Cépaduès Editions, 2004.

    Google Scholar 

  51. S. Lallich and O. Teytaud. É valuation et validation de l’intérêt des règles d’association. Revue des Nouvelles Technologies de l’Information (Mesures de Qualité pour la Fouille de Données), (RNTI-E-1):193-217, 2004.

    Google Scholar 

  52. S. Lallich, B. Vaillant, and P. Lenca. Parametrised measures for the evaluation of association rule interestingness. In J. Janssen and P. Lenca, editors, The XIth International Symposium on Applied Stochastic Models and Data Analysis, pages 220-229, Brest, France, 2005.

    Google Scholar 

  53. N. Lavrac, P. Flach, and B. Zupan. Rule evaluation measures: A unifying view. In S. Dzeroski and P. Flach, editors, Ninth International Workshop on Inductive Logic Programming, volume 1634 of Lecture Notes in Computer Science, pages 174-185. Springer-Verlag, 1999.

    Google Scholar 

  54. E. Le Saux, P. Lenca, J-P. Barthélemy, and P. Picouet. Updating a rule basis under cognitive constraints: the COMAPS tool. In The Seventeenth European Annual Conference on Human Decision Making and Manual Control, pages 3-9, December 1998.

    Google Scholar 

  55. E. Le Saux, P. Lenca, and P. Picouet. Dynamic adaptation of rules bases under cognitive constraints. European Journal of Operational Research, 136(2):299-309,2002.

    Article  MATH  Google Scholar 

  56. R. Lehn, F. Guillet, P. Kuntz, H. Briand, and J. Philippé. Felix: An interactive rule mining interface in a KDD process. In P. Lenca, editor, Human Centered Processes, pages 169-174, Brest, France, 1999.

    Google Scholar 

  57. P. Lenca, P. Meyer, P. Picouet, B. Vaillant, and S. Lallich. Critères d’évaluation des mesures de qualité en ecd. Revue des Nouvelles Technologies de l’Information (Entreposage et Fouille de Données), (1):123-134, 2003.

    Google Scholar 

  58. P. Lenca, P. Meyer, B. Vaillant, and S. Lallich. A multicriteria decision aid for interestingness measure selection. Technical Report LUSSI-TR-2004-01-EN, Département LUSSI, ENST Bretagne, 2004.

    Google Scholar 

  59. P. Lenca, P. Meyer, B. Vaillant, and P. Picouet. Aide multicritère à la décision pour évaluer les indices de qualité des connaissances - modélisation des préférences de l’utilisateur. In M.-S. Hacid, Y. Kodratoff, and D. Boulanger, editors, Extraction et gestion des connaissances, volume 17 of RSTI-RIA, pages 271-282. Lavoisier, 2003.

    Google Scholar 

  60. P. Lenca, P. Meyer, B. Vaillant, P. Picouet, and S. Lallich. Évaluation et analyse multicritère des mesures de qualité des règles d’association. Revue des Nouvelles Technologies de l’Information (Mesures de Qualité pour la Fouille de Données), (RNTI-E-1):219-246, 2004.

    Google Scholar 

  61. P. Lenca, B. Vaillant, and S. Lallich. On the robustness of association rules. In IEEE International Conference on Cybernetics and Intelligent Systems, Bangkok, Thailand, 2006.

    Google Scholar 

  62. I.C. Lerman. Classification et analyse ordinale des données. Dunod, 1970.

    Google Scholar 

  63. I.C. Lerman and J. Azé. Une mesure probabiliste contextuelle discriminante de qualité des règles d’association. In M.-S. Hacid, Y. Kodratoff, and D. Boulanger, editors, Extraction et gestion des connaissances, volume 17 of RSTI-RIA, pages 247-262. Lavoisier, 2003.

    Google Scholar 

  64. I.C. Lerman, R. Gras, and H. Rostam. Elaboration d’un indice d’implication pour les données binaires, i et ii. Mathématiques et Sciences Humaines, (74, 75):5-35, 5-47, 1981.

    Google Scholar 

  65. B. Liu, W. Hsu, and S. Chen. Using general impressions to analyze discovered classification rules. In Third International Conference on Knowledge Discovery and Data Mining, pages 31-36, 1997.

    Google Scholar 

  66. B. Liu, W. Hsu, S. Chen, and Y. Ma. Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, 15(5):47-55, 2000.

    Article  Google Scholar 

  67. B. Liu, W. Hsu, K. Wang, and S. Chen. Visually aided exploration of interesting association rules. In Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, pages 380-389. Springer Verlag, 1999.

    Google Scholar 

  68. J. Loevinger. A systemic approach to the construction and evaluation of tests of ability. Psychological monographs, 61(4), 1947.

    Google Scholar 

  69. J.-L. Marichal, P. Meyer, and M. Roubens. Sorting multi-attribute alternatives: The tomaso method. Computers & Operations Research, (32):861-877, 2005.

    Google Scholar 

  70. K. McGarry. A survey of interestingness measures for knowledge discovery. Knowledge Engineering Review Journal, 20(1):39-61, 2005.

    Article  Google Scholar 

  71. M. Ohsaki, Y. Sato, S. Kitaguchi, H. Yokoi, and T. Yamaguchi. Comparison between objective interestingness measures and real human interest in medical data mining. In R. Orchard, C. Yang, and M. Ali, editors, The 17th international conference on Innovations in Applied Artificial Intelligence, volume 3029 of Lecture Notes in Artificial Intelligence, pages 1072-1081. Springer-Verlag, 2004.

    Google Scholar 

  72. B. Padmanabhan. The interestingness paradox in pattern discovery. Journal of Applied Statistics, 31(8):1019-1035, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  73. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In C. Beeri and P. Buneman, editors, The 7th International Conference on Database Theory, volume 1540 of Lecture Notes in Computer Science, pages 398-416, Jerusalem, Israel, 1999. Springer.

    Google Scholar 

  74. K. Pearson. Mathematical contributions to the theory of evolution. iii. regression, heredity and panmixia. Philosophical Transactions of the Royal Society, A, 1896.

    Google Scholar 

  75. G. Piatetsky-Shapiro. Discovery, analysis and presentation of strong rules. In G. Piatetsky-Shapiro and W.J. Frawley, editors, Knowledge Discovery in Databases, pages 229-248. AAAI/MIT Press, 1991.

    Google Scholar 

  76. P. Picouet and P. Lenca. Bases de données et internet, chapter Extraction de connaissances à partir des données, pages 395-420. Hermes Science, 2001.

    Google Scholar 

  77. M. Plasse, N. Niang, G. Saporta, and L. Leblond. Une comparaison de certains indices de pertinence des règles d’association. In G. Ritschard and C. Djeraba, editors, Extraction et gestion des connaissances, volume 1-2, pages 561-568. Cépaduès- Éditions, 2006.

    Google Scholar 

  78. F. Poulet. Visualization in data-mining and knowledge discovery. In P. Lenca, editor, Human Centered Processes, pages 183-191, Brest, France, 1999.

    Google Scholar 

  79. F. Poulet. Towards visual data mining. In 6th International Conference on Enterprise Information Systems, pages 349-356, 2004.

    Google Scholar 

  80. J. Rauch and M. Simunek. Mining for 4ft association rules by 4ft-miner. In Proceeding of the International Conference On Applications of Prolog, pages 285-294, Tokyo, Japan, 2001.

    Google Scholar 

  81. M. Sebag and M. Schoenauer. Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases. In J. Boose, B. Gaines, and M. Linster, editors, The European Knowledge Acquisition Workshop, pages 28-1-28-20. Gesellschaft für Mathematik und Datenverarbeitung mbH, 1988.

    Google Scholar 

  82. A. Silberschatz and A. Tuzhilin. On subjective measures of interestingness in knowledge discovery. In Knowledge Discovery and Data Mining, pages 275-281, 1995.

    Google Scholar 

  83. A. Silberschatz and A. Tuzhilin. User-assisted knowledge discovery: How much should the user be involved. In ACM-SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1996.

    Google Scholar 

  84. S.J. Simoff. Towards the development of environments for designing visualisation support for visual data mining. In S.J. Simoff, M. Noirhomme-Fraiture, and M.H. Böhlen, editors, International Workshop on Visual Data Mining in cunjunction with ECML/PKDD’01, pages 93-106, 2001.

    Google Scholar 

  85. E. Suzuki. In pursuit of interesting patterns with undirected discovery of ex- ception rules. In S. Arikawa and A. Shinohara, editors, Progresses in Discovery Science, volume 2281 of Lecture Notes in Computer Science, pages 504-517. Springer-Verlag, 2002.

    Google Scholar 

  86. E. Suzuki. Discovering interesting exception rules with rule pair. In ECML/PKDD Workshop on Advances in Inductive Rule Learning, pages 163-178,2004.

    Google Scholar 

  87. P.-N. Tan, V. Kumar, and J. Srivastava. Selecting the right interestingness measure for association patterns. In The Eighth ACM SIGKDD International Conference on KDD, pages 32-41, 2002.

    Google Scholar 

  88. P-N. Tan, V. Kumar, and J. Srivastava. Selecting the right objective measure for association analysis. Information Systems, 4(29):293-313, 2004.

    Article  Google Scholar 

  89. A. Totohasina, H. Ralambondrainy, and J. Diatta. Notes sur les mesures proba-bilistes de la qualité des règles d’association: un algorithme efficace d’extraction des règles d’association implicative. In 7ème Colloque Africain sur la Recherche en Informatique, pages 511-518, 2004.

    Google Scholar 

  90. B. Vaillant. Evaluation de connaissances: le problème du choix d’une mesure de qualité en extraction de connaissances à partir des données. Master’s thesis, Ecole Nationale Supérieure des Télécommunications de Bretagne, 2002.

    Google Scholar 

  91. B. Vaillant, P. Lenca, and S. Lallich. Association rule interestingness measures: an experimental study. Technical Report LUSSI-TR-2004-02-EN, Département LUSSI, ENST Bretagne, 2004.

    Google Scholar 

  92. B. Vaillant, P. Lenca, and S. Lallich. A clustering of interestingness measures. In E. Suzuki and S. Arikawa, editors, Discovery Science, volume 3245 of Lecture Notes in Artificial Intelligence, pages 290–297, Padova, Italy, 2004. SpringerVerlag.

    Google Scholar 

  93. B. Vaillant, P. Picouet, and P. Lenca. An extensible platform for rule quality measure benchmarking. In R. Bisdorff, editor, Human Centered Processes, pages 187–191, 2003.

    Google Scholar 

  94. H. Xiong, P. Tan, and V. Kumar. Mining strong affinity association patterns in data sets with skewed support distribution. In Third IEEE International Conference on Data Mining, pages 387–394, Melbourne, Florida, 2003.

    Google Scholar 

  95. T. Zhang. Association rules. In T. Terano, H. Liu, and A.L.P. Chen, editors, 4th Pacific-Asia Conference Knowledge Discovery and Data Mining, Current Issues and New Applications, volume 1805 of Lecture Notes in Computer Science, Kyoto, Japan, 2000. Springer.

    Google Scholar 

  96. A. Zimmermann and L. De Raedt. CorClass: Correlated association rule mining for classification. In E. Suzuki and S. Arikawa, editors, Discovery Science, volume 3245 of Lecture Notes in Artificial Intelligence, pages 60–72, Padova, Italy, 2004. Springer-Verlag..

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TAMCIC CNRS 2872, GET/ENST, Bretagne, France

    Philippe Lenca

  2. TAMCIC CNRS 2872, GET/ENST, Bretagne, France

    Benoît Vaillant

  3. University of Luxemburg, Luxemburg, Germany

    Patrick Meyer

  4. ERIC, University of Lyon 2, France

    Stephane Lallich

Authors
  1. Philippe Lenca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benoît Vaillant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrick Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stephane Lallich
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. LINA-CNRS FRE 2729, Ecole polytechnique de l'université de Nantes, Rue Christian-Pauc-La Chantrerie, 60601, 44306, NANTES Cedex 3, France

    Fabrice J. Guillet

  2. Department of Computer Science, University of Regina, SK S4S 0A2, Regina, Canada

    Howard J. Hamilton

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Lenca, P., Vaillant, B., Meyer, P., Lallich, S. (2007). Association Rule Interestingness Measures: Experimental and Theoretical Studies. 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_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-44918-8_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44911-9

  • Online ISBN: 978-3-540-44918-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation