Multicriteria attractiveness evaluation of decision and association rules

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5656 LNCS, Issue , 2009, Pages 197-274

  Szczȩch, Izabela ^a  

^a POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

APRIORI; DATA SETS; EVALUATION SPACE; INTERESTINGNESS MEASURES; MULTI-CRITERIA; MULTI-CRITERIA APPROACH; MULTI-CRITERIA EVALUATION; RULE EVALUATION;

ASSOCIATIVE PROCESSING;

ASSOCIATION RULES;

EID: 77951998372     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03281-3_8     Document Type: Conference Paper

References (99)

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)
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)
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)
4. Bramer, M.: Principles of Data Mining. Springer, New York (2007)
5. Briand, L., El Emam, K., Morasca, S.: On the Application of Measurement Theory in Software Engineering. Empirical Software Engineering 1, 61-88 (1995) (Pubitemid 126712624)
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)
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)
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)
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)
10. Carnap, R.: Logical Foundations of Probability, 2nd edn. University of Chicago Press, Chicago (1962)
11. Christensen, D.: Measuring confirmation. Journal of Philosophy XCVI, 437-461 (1999)
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)
13. Cichosz, P.: Systemy ucza̧ce siȩ. Warszawa, WNT (2000)
14. Cios, K., Pedrycz, W., Świniarski, R.: Data mining methods for knowledge discovery. Kluwer Academic Publishers, Dordrecht (1999)
15. Crupi, V., Tentori, K., Gonzalez, M.: On Bayesian Theories of Evidential Support: Theoretical and empirical issues. Philosophy of Science (to appear)
16. Dhar, V., Tuzhilin, A.: Abstract-driven pattern discovery in databases. IEEE Transactions on Knowledge and Data Engineering 5(6) (1993)
17. Earman, J.: Bayes or Bust: A Critical Examination of Bayesian Confirmation Theory. MIT Press, Cambridge (1992)
18. Eells, E.: Rational Decision and Causality. Cambridge University Press, Cambridge (1982)
19. Eells, E., Fitelson, B.: Symmetries and asymmetries in evidential support. Philosophical Studies 107(2), 129-142 (2002)
20. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1996)
21. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: From data mining to knowledge discovery. In: [20], pp. 1-36 (1999)
22. Fitelson, B.: Studies in Bayesian Confirmation Theory. Ph.D. Thesis, University of Wisconsin, Madison (2001)
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)
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)
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)
26. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3) (2006) (Pubitemid 44498422)
27. Good, I.J.: The best explicatum for weight of evidence. Journal of Statistical Computation and Simulation 19, 294-299 (1984)
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)
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)
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)
31. Guillet, F., Hamilton, H.J. (eds.): Quality Measures in Data Mining. Studies in Computational Intelligence, vol.43. Springer, Heidelberg (2006)
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)
33. Han, J., Kamber, M.: Data mining: Concepts and techniques. Morgan Kaufman Pub., San Francisco (2000)
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)
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)
36. Hempel, C.G.: Studies in the logic of confirmation (I). Mind 54, 1-26 (1945)
37. Hilderman, R., Hamilton, H.: Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, Dordrecht (2001)
38. Horwich, P.: Probability and Evidence. Cambridge University Press, Cambridge (1982)
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)
40. International Business Machines, IBM Intelligent Miner User's Guide, Version 1, Release 1 (1996)
41. Jeffrey, H.: Some tests of significance treated by theory of probability. Proceedings of the Cambridge Philosophical Society 31, 203-222 (1935)
42. Joyce, J.: The Foundations of Causal Decision Theory. Cambridge University Press, Cambridge (1999)
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)
44. Keynes, J.: A Treatise on Probability. Macmillan, London (1921)
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)
46. Kemeny, J., Oppenheim, P.: Degrees of factual support. Philosophy of Science 19, 307-324 (1952)
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)
48. Kubat, M., Bratko, I., Michalski, R.S.: Review of machine learning methods. In: [54], pp. 3-70
49. Kyburg, H.: Recent work in inductive logic. In: Recent Work In Philosophy, pp. 87-150. Rowman & Allanheld, Lanham (1983)
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) (Pubitemid 47374386)
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)
52. Mackie, J.L.: The relevance citerion of confirmation. The British Journal for the Philosophy of Science 20, 27-40 (1969)
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)
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)
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)
56. Michalski, R.S., Bratko, I., Kubat, M. (eds.): Machine learning and data mining. John Wiley & Sons, Chichester (1998)
57. Michell, J.: Measurement scales and statistics: a clash of paradigms. Psychological Bulletin 3, 398-407 (1986)
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)
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)
60. Morzy, T.: Odkrywanie asocjacji: algorytmy i struktury danych. Ośrodek Wydawnictw Naukowych, Poznań (2004)
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)
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)
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)
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)
66. Pawlak, Z.: Rough Sets. Int. Journal of Computer and Information Science 11, 341-356 (1982)
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) (Pubitemid 16630925)
68. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht (1991)
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)
70. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3-27 (2007);
71. Rough sets: Some extensions. Information Sciences 177(1), 28-40;
72. Rough sets and boolean reasoning. Information Sciences 177(1), 41-73
73. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman, San Francisco (1988)
74. Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, ch. 12. AAAI/MIT Press (1991)
75. Pollard, S.: Milne's measure of confirmation. Analysis 59, 335-337 (1999)
76. Popper, K.R.: The Logic of Scientific Discovery. Hutchinson, London (1959)
77. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
78. Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81-106 (1986)
79. Reiter, R.: On closed world data bases. In: Gallaire, H., Minker, J. (eds.) Logic and Data Bases, pp. 119-140. Plenum, New York (1978)
80. Rosenkrantz, R.: Bayesian confirmation: paradise regained. The British Journal for the Philosophy of Science 45, 467-476 (1994)
81. Schlesinger, G.: Measuring degrees of confirmation. Analysis 55, 208-212 (1995)
82. Schum, D.: The Evidential Foundations of Probabilistic Reasoning. Wiley, New York (1994)
83. Silberschatz, A., Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Transactions on Knowledge and Data Engineering 8(6), 970-974 (1996) (Pubitemid 126774153)
84. 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)
85. 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)
86. 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)
87. 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)
88. 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)
89. Stefanowski, J.: Algorytmy indukcji reguł decyzyjnych w odkrywaniu wiedzy. Rozprawa habilitacyjna, Politechnika Poznańska, Instytut Informatyki, Poznań (2001)
90. Stevens, S.S.: On the theory of scales of measurement. Science 103, 677-680 (1946)
91. Szczepaniak, P.S., Segovia, J., Kacprzyk, J., Zadeh, L.A. (eds.): Intelligent Exploration of the Web. Springer, Heidelberg (2003)
92. Szczepaniak, P.S.: Obliczenia inteligentne, szybkie przekształ cenia i klasyfikatory. Akademicka Oficyna Wydawnicza EXIT, Warszawa (2004)
93. Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Inf. Syst. 29(4), 293-313 (2004)
94. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education, Inc., London (2006)
95. Webb, G.I.: OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research 3, 431-465 (1995)
96. 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)
97. Zakrzewicz, M.: Data Mining i odkrywanie wiedzy w bazach danych. In: Materiały konferencyjne III Konferencji Polskiej Grupy Użytkowników Systemu Oracle, Zakopane (1997)
98. Zakrzewicz, M.: Optymalizacja wykonania zapytań eksploracyjnych w systemach baz danych, Rozprawa habilitacyjna, Politechnika Poznańska, Instytut Informatyki, Poznań (2004)
99. Zembrowicz, R., Żytkow, J.: From contingency tables to various forms of knowledge in databases. In: [20], pp. 329-352