Skip to main content

Multicriteria Attractiveness Evaluation of Decision and Association Rules

  • Chapter
Transactions on Rough Sets X

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 5656))

Abstract

The work is devoted to multicriteria approaches to rule evaluation. It analyses desirable properties (in particular the property M, property of confirmation and hypothesis symmetry) of popular interestingness measures of decision and association rules. Moreover, it analyses relationships between the considered interestingness measures and enclosure relationships between the sets of non-dominated rules in different evaluation spaces. It’s main result is a proposition of a multicriteria evaluation space in which the set of non-dominated rules will contain all optimal rules with respect to any attractiveness measure with the property M. By determining the area of rules with desirable value of a confirmation measure in the proposed multicriteria evaluation space one can narrow down the set of induced rules only to the valuable ones. Furthermore, the work presents an extension of an apriori-like algorithm for generation of rules with respect to attractiveness measures possessing valuable properties and shows some applications of the results to analysis of rules induced from exemplary datasets.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Azevedo, P.J., Jorge, A.M.: Comparing rule Measures for Predictive Association Rules. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 510–517. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, A.: Mining associations between sets of items in massive databases. In: Proc. of the 1993 ACM-SIGMOD Int’l. conf. on Management of Data, pp. 207–216 (1993)

    Google Scholar 

  3. Bayardo, R.J., Agrawal, R.: Mining the most interesting rules. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Proc. of the Fifth ACM-SIGKDD Int’l. conf. on Knowledge Discovery and Data Mining. Advances in Knowledge Discovery and Data Mining, pp. 145–154. AAAI/MIT Press, Cambridge (1996)

    Google Scholar 

  4. Bramer, M.: Principles of Data Mining. Springer, New York (2007)

    MATH  Google Scholar 

  5. Briand, L., El Emam, K., Morasca, S.: On the Application of Measurement Theory in Software Engineering. Empirical Software Engineering 1, 61–88 (1995)

    Article  Google Scholar 

  6. Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proc. of the 1997 ACM-SIGMOD Int’l. conf. on the Management of Data, pp. 255–264 (1997)

    Google Scholar 

  7. Brzezinska, I., Slowinski, R.: Monotonicity of a Bayesian confirmation measure in rule support and confidence. In: Recent Developments in Artificial Intelligence Methods, Gliwice. AI-METH Series, pp. 39–42 (2005)

    Google Scholar 

  8. Brzezinska, I., Greco, S., Slowinski, R.: Investigation of monotone link between confirmation measures and rule support and confidence. Research Report RA-025/05, Institute of Computing Science, Poznan University of Technology, Poznan (2005)

    Google Scholar 

  9. Brzezińska, I., Greco, S., Słowiński, R.: Mining Pareto–optimal rules with respect to support and anti-support. Engineering Applications of Artificial Intelligence 20(5), 587–600 (2007)

    Article  Google Scholar 

  10. Carnap, R.: Logical Foundations of Probability, 2nd edn. University of Chicago Press, Chicago (1962)

    MATH  Google Scholar 

  11. Christensen, D.: Measuring confirmation. Journal of Philosophy XCVI, 437–461 (1999)

    Google Scholar 

  12. Clark, P., Boswell, P.: Rule induction with CN2: some recent improvements. In: Machine Learning: Proc. of the Fifth European Conference, pp. 151–163 (1991)

    Google Scholar 

  13. Cichosz, P.: Systemy ucza̧ce siȩ. Warszawa, WNT (2000)

    Google Scholar 

  14. Cios, K., Pedrycz, W., Świniarski, R.: Data mining methods for knowledge discovery. Kluwer Academic Publishers, Dordrecht (1999)

    MATH  Google Scholar 

  15. Crupi, V., Tentori, K., Gonzalez, M.: On Bayesian Theories of Evidential Support: Theoretical and empirical issues. Philosophy of Science (to appear)

    Google Scholar 

  16. Dhar, V., Tuzhilin, A.: Abstract-driven pattern discovery in databases. IEEE Transactions on Knowledge and Data Engineering 5(6) (1993)

    Google Scholar 

  17. Earman, J.: Bayes or Bust: A Critical Examination of Bayesian Confirmation Theory. MIT Press, Cambridge (1992)

    Google Scholar 

  18. Eells, E.: Rational Decision and Causality. Cambridge University Press, Cambridge (1982)

    Book  MATH  Google Scholar 

  19. Eells, E., Fitelson, B.: Symmetries and asymmetries in evidential support. Philosophical Studies 107(2), 129–142 (2002)

    Article  Google Scholar 

  20. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1996)

    Google Scholar 

  21. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: From data mining to knowledge discovery. In: [20], pp. 1–36 (1999)

    Google Scholar 

  22. Fitelson, B.: Studies in Bayesian Confirmation Theory. Ph.D. Thesis, University of Wisconsin, Madison (2001)

    Google Scholar 

  23. Francisci, D., Collard, M.: Multi-criteria evaluation of interesting dependencies according to a data mining approach. In: Congress on Evolutionary Computation, Canberra, Australia, 12, pp. 1568–1574. IEEE Computer Society Press, Los Alamitos (2003)

    Google Scholar 

  24. Frawley, W.J., Piatetsky-Shapiro, G., Matheus, C.J.: Knowledge discovery in databases: an overview. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 1–27. AAAI/MIT Press (1991)

    Google Scholar 

  25. Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization. In: Proc. of the 1996 ACM-SIGMOD Int’l. conf. on the Management of Data, pp. 13–23 (1996)

    Google Scholar 

  26. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3) (2006)

    Google Scholar 

  27. Good, I.J.: The best explicatum for weight of evidence. Journal of Statistical Computation and Simulation 19, 294–299 (1984)

    Article  Google Scholar 

  28. Greco, S., Pawlak, Z., Słowiński, R.: Can Bayesian confirmation measures be useful for rough set decision rules? Engineering Applications of Artificial Intelligence 17, 345–361 (2004)

    Article  Google Scholar 

  29. Greco, S., Słowiński, R., Szczȩch, I.: Analysis of monotonicity properties of some rule interestingness measures. In: Materiały II Krajowej Konferencji nt. Technologie Przetwarzania Danych. Poznań, 24-26.09.2007, Wydawnictwo Politechniki Poznańskiej, Poznań, pp. 151–161 (2007)

    Google Scholar 

  30. Greco, S., Matarazzo, E., Słowiński, R.: Parameterized rough set model using rough membership and Bayesian confirmation measures. International Journal of Approximate Reasoning 49(2), 285–300 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  31. Guillet, F., Hamilton, H.J. (eds.): Quality Measures in Data Mining. Studies in Computational Intelligence, vol. 43. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  32. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. ACM SIGMOD Conference on Management of Data, pp. 1–12 (2000)

    Google Scholar 

  33. Han, J., Kamber, M.: Data mining: Concepts and techniques. Morgan Kaufman Pub., San Francisco (2000)

    MATH  Google Scholar 

  34. Hebert, C., Cremilleux, B.: A Unified View of Objective Interestingness Measures. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 533–547. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  35. Heckerman, D.: An axiomatic framework for belief updates. In: Kanal, L., Lemmer, J. (eds.) Unconfidence in Artificial Intelligence, vol. 2, pp. 11–22. Elsevier Science Publishers, New York (1988)

    Google Scholar 

  36. Hempel, C.G.: Studies in the logic of confirmation (I). Mind 54, 1–26 (1945)

    Google Scholar 

  37. Hilderman, R., Hamilton, H.: Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, Dordrecht (2001)

    Book  MATH  Google Scholar 

  38. Horwich, P.: Probability and Evidence. Cambridge University Press, Cambridge (1982)

    MATH  Google Scholar 

  39. Horvitz, E., Heckerman, D.: The inconsistent use of certainty measures in artificial intelligence research. In: Kanal, L., Lemmer, J. (eds.) Uncertainty in Artificial Intelligence, vol. (1), pp. 137–151. Elsevier Science Publishers, New York (1986)

    Chapter  Google Scholar 

  40. International Business Machines, IBM Intelligent Miner User’s Guide, Version 1, Release 1 (1996)

    Google Scholar 

  41. Jeffrey, H.: Some tests of significance treated by theory of probability. Proceedings of the Cambridge Philosophical Society 31, 203–222 (1935)

    Article  Google Scholar 

  42. Joyce, J.: The Foundations of Causal Decision Theory. Cambridge University Press, Cambridge (1999)

    Book  MATH  Google Scholar 

  43. Kamber, M., Shingal, R.: Evaluating the interestingness of characteristic rules. In: Proccedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996), Portland, Oregon, pp. 263–266 (1996)

    Google Scholar 

  44. Keynes, J.: A Treatise on Probability. Macmillan, London (1921)

    MATH  Google Scholar 

  45. Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: Proc. of Third Int’l. conf. on Information and Knowledge Management (CIKM 1994), pp. 401–407. ACM Press, New York (1994)

    Google Scholar 

  46. Kemeny, J., Oppenheim, P.: Degrees of factual support. Philosophy of Science 19, 307–324 (1952)

    Article  Google Scholar 

  47. Kohavi, R.: Scaling up the accuracy of Naive–Bayes Classifiers: a decision–tree hybrid. In: Proc. of the 2nd Int’l. conf. on Knowledge Discovery and Data Mining (1996)

    Google Scholar 

  48. Kubat, M., Bratko, I., Michalski, R.S.: Review of machine learning methods. In: [54], pp. 3–70

    Google Scholar 

  49. Kyburg, H.: Recent work in inductive logic. In: Recent Work In Philosophy, pp. 87–150. Rowman &Allanheld, Lanham (1983)

    Google Scholar 

  50. Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid. European Journal of Operational Research 184(2), 610–626 (2008)

    Google Scholar 

  51. Łukasiewicz, J.: Die logischen Grundlagen der Wahrscheinlichkeitsrechnung. In: Borkowski, L. (ed.) Jan Łukasiewicz –Selected Works, pp. 16–63. North Holland Publishing Company/Polish Scientific Publishers, Amsterdam, London (1913) (1970)

    Google Scholar 

  52. Mackie, J.L.: The relevance citerion of confirmation. The British Journal for the Philosophy of Science 20, 27–40 (1969)

    Article  MATH  Google Scholar 

  53. McGarry, K.: A survey of interestingness measures for knowledge discovery. In: The Knowledge Engineering Review, vol. 20(1), pp. 39–61. Cambridge University Press, Cambridge (2005A)

    Google Scholar 

  54. Michalski, R.S.: A theory and methodology of inductive learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, pp. 83–134. Morgan Kaufman, San Francisco (1983)

    Chapter  Google Scholar 

  55. Michalski, R.S.: Machine Learning, Data Mining and Knowledge Discovery. Principles and Applications. In: Tutorials of Intelligent Information Systems, IIS 1997, Zakopane, IPI. PAN Press (1997)

    Google Scholar 

  56. Michalski, R.S., Bratko, I., Kubat, M. (eds.): Machine learning and data mining. John Wiley & Sons, Chichester (1998)

    Google Scholar 

  57. Michell, J.: Measurement scales and statistics: a clash of paradigms. Psychological Bulletin 3, 398–407 (1986)

    Article  Google Scholar 

  58. Morimoto, Y., Fukuda, T., Matsuzawa, H., Tokuyama, T., Yoda, K.: Algorithms for mining association rules for binary segmentation of huge categorical databases. In: Proc. of the 24th Very Large Data Bases conf., pp. 380–391 (1998)

    Google Scholar 

  59. Morishita, S.: On classification and regression. In: Arikawa, S., Motoda, H. (eds.) DS 1998. LNCS (LNAI), vol. 1532, pp. 40–57. Springer, Heidelberg (1998)

    Google Scholar 

  60. Morzy, T.: Odkrywanie asocjacji: algorytmy i struktury danych. Ośrodek Wydawnictw Naukowych, Poznań (2004)

    Google Scholar 

  61. Morzy, T., Zakrzewicz, M.: Data mining. In: Błażewicz, J., Kubiak, W., Morzy, T., Rusinkiewicz, M.E. (eds.) Handbook on data management in information systems, pp. 487–565. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  62. Morzy, M.: Eksploracja danych - przegla̧d dostȩpnych metod i dziedzin zastosowań. In: VI edycja Hurtownia danych i business intelligence, Centrum Promocji Informatyki, Warszawa, Poland (2006)

    Google Scholar 

  63. Morzy, M.: Oracle Data Mining - odkrywanie wiedzy w dużych wolumenach danych. In: XI Krajowa Konferencja PLOUG 2005 Systemy informatyczne. Projektowanie, implementowanie, eksploatowanie, Zakopane, Poland, pp. 18–21 (2005)

    Google Scholar 

  64. Msweb dataset, http://kdd.ics.uci.edu/databases/msweb/msweb.html

  65. Nicod, J.: Le probleme de la logique de l’induction. Alcan, Paris (1923)

    Google Scholar 

  66. Pawlak, Z.: Rough Sets. Int. Journal of Computer and Information Science 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  67. Pawlak, Z., Słowiński, K., Słowiński, R.: Rough classification of patients after highly selected vagotomy for duodenal ulcer. International J. Man-Machine Studies 24, 413–433 (1986)

    Article  Google Scholar 

  68. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht (1991)

    Book  MATH  Google Scholar 

  69. Pawlak, Z.: Some Issues on Rough Sets. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 1–58. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  70. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007); Rough sets: Some extensions. Information Sciences 177(1), 28–40; Rough sets and boolean reasoning. Information Sciences 177(1), 41–73

    Google Scholar 

  71. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman, San Francisco (1988)

    MATH  Google Scholar 

  72. Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, ch. 12. AAAI/MIT Press (1991)

    Google Scholar 

  73. Pollard, S.: Milne’s measure of confirmation. Analysis 59, 335–337 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  74. Popper, K.R.: The Logic of Scientific Discovery. Hutchinson, London (1959)

    MATH  Google Scholar 

  75. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  76. Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)

    Google Scholar 

  77. Reiter, R.: On closed world data bases. In: Gallaire, H., Minker, J. (eds.) Logic and Data Bases, pp. 119–140. Plenum, New York (1978)

    Google Scholar 

  78. Rosenkrantz, R.: Bayesian confirmation: paradise regained. The British Journal for the Philosophy of Science 45, 467–476 (1994)

    Article  MathSciNet  Google Scholar 

  79. Schlesinger, G.: Measuring degrees of confirmation. Analysis 55, 208–212 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  80. Schum, D.: The Evidential Foundations of Probabilistic Reasoning. Wiley, New York (1994)

    Google Scholar 

  81. Silberschatz, A., Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Transactions on Knowledge and Data Engineering 8(6), 970–974 (1996)

    Article  Google Scholar 

  82. Słowiński, R.: Rozszerzenie teorii zbiorów przybliżonych na atrybuty ze skala̧ preferencji, w: Materiały konferencji Informatyka teoretyczna: metody analizy informacji niekompletnej i rozproszonej, Białystok, pp. 114–128 (2000)

    Google Scholar 

  83. Słowiński, R., Greco, S.: Measuring attractiveness of rules from the viewpoint of knowledge representation, prediction and efficiency of intervention. In: Szczepaniak, P., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 11–22. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  84. Słowiński, R., Brzezińska, I., Greco, S.: Application of Bayesian Confirmation Measures for Mining Rules from Support–confidence Pareto-optimal set. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS, vol. 4029, pp. 1018–1026. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  85. Słowiński, R., Szczȩch, I., Urbanowicz, M., Greco, S.: Experiments with induction of association rules with respect to support and anti-support. Research Report RA-018/06, Institute of Computing Science, Poznań University of Technology, Poznań (2006)

    Google Scholar 

  86. Słowiński, R., Szczȩch, I., Urbanowicz, M., Greco, S.: Mining Association Rules with respect to Support and Anti-support - Experimental Results. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 534–542. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  87. Stefanowski, J.: Algorytmy indukcji reguł decyzyjnych w odkrywaniu wiedzy. Rozprawa habilitacyjna, Politechnika Poznańska, Instytut Informatyki, Poznań (2001)

    Google Scholar 

  88. Stevens, S.S.: On the theory of scales of measurement. Science 103, 677–680 (1946)

    Article  MATH  Google Scholar 

  89. Szczepaniak, P.S., Segovia, J., Kacprzyk, J., Zadeh, L.A. (eds.): Intelligent Exploration of the Web. Springer, Heidelberg (2003)

    Google Scholar 

  90. Szczepaniak, P.S.: Obliczenia inteligentne, szybkie przekształcenia i klasyfikatory. Akademicka Oficyna Wydawnicza EXIT, Warszawa (2004)

    Google Scholar 

  91. Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Inf. Syst. 29(4), 293–313 (2004)

    Article  Google Scholar 

  92. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education, Inc., London (2006)

    Google Scholar 

  93. Webb, G.I.: OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research 3, 431–465 (1995)

    MATH  Google Scholar 

  94. Yao, Y.Y., Zhong, N.: An analysis of quantitative measures associated with rules. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 479–488. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  95. Zakrzewicz, M.: Data Mining i odkrywanie wiedzy w bazach danych. In: Materiały konferencyjne III Konferencji Polskiej Grupy Użytkowników Systemu Oracle, Zakopane (1997)

    Google Scholar 

  96. Zakrzewicz, M.: Optymalizacja wykonania zapytań eksploracyjnych w systemach baz danych, Rozprawa habilitacyjna, Politechnika Poznańska, Instytut Informatyki, Poznań (2004)

    Google Scholar 

  97. Zembrowicz, R., Żytkow, J.: From contingency tables to various forms of knowledge in databases. In: [20], pp. 329–352

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Szczȩch, I. (2009). Multicriteria Attractiveness Evaluation of Decision and Association Rules. In: Peters, J.F., Skowron, A., Wolski, M., Chakraborty, M.K., Wu, WZ. (eds) Transactions on Rough Sets X. Lecture Notes in Computer Science, vol 5656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03281-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03281-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03280-6

  • Online ISBN: 978-3-642-03281-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics