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Association Rule and Decision Tree based Methodsfor Fuzzy Rule Base Generation

Authors: Ferenc Peter Pach, Janos Abonyi

Abstract:

This paper focuses on the data-driven generation of fuzzy IF...THEN rules. The resulted fuzzy rule base can be applied to build a classifier, a model used for prediction, or it can be applied to form a decision support system. Among the wide range of possible approaches, the decision tree and the association rule based algorithms are overviewed, and two new approaches are presented based on the a priori fuzzy clustering based partitioning of the continuous input variables. An application study is also presented, where the developed methods are tested on the well known Wisconsin Breast Cancer classification problem.

Keywords:

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082149

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[1] J. Abonyi, B. Feil, S. Nemeth, and P. Arva. Modified Gath-Geva clustering for fuzzy segmentation of multivariate time-series. Fuzzy Sets and Systems, Data Mining Special Issue, pages in print, avaiable on-line from Science Direct, 2005.
[2] J. M. Adamo. Fuzzy decision trees. Fuzzy Sets and Systems, 4(3):207- 219, 1980.
[3] R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216, 1993.
[4] R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A.I. Verkamo. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, pages 307-328. AAAI/MIT Press, 1996.
[5] R. Agrawal and R. Srikant. Fast algorithm for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, pages 487-499, 1994.
[6] Jean-Roger Le Gall Anke Neumann, Josiane Holstein and Eric Lepage. Measuring performance in health care: case-mix adjustment by boosted decision trees. Artificial Intelligence in Medicine, 32(2):97-113, 2004.
[7] Jan Jantzen Hubertus Axer Beth Bjerregaard Athanasios Tsakonas, Georgios Dounias and Diedrich Graf von Keyserlingk. Evolving rulebased systems in two medical domains using genetic programming. Artificial Intelligence in Medicine, 32(3):195-216, 2004.
[8] W-H. Au and K.C.C. Chan. An effective algorithm for discovering fuzzy rules in relational databases. In Proceedings of the 7th IEEE International Conference on Fuzzy Systems, pages 1314-1319, 1998.
[9] W-H. Au and K.C.C. Chan. Farm: A data mining system for discovering fuzzy association rules. In Proceedings of the 8th IEEE International Conference on Fuzzy Systems, pages 1217-1222, 1999.
[10] Elena Baralis and Silvia Chiusano. Essential classification rule sets. ACM Transactions on Database Systems, 29(4):635674, 2004.
[11] Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):66-75, 2000.
[12] R.J. Bayardo. Efficiently mining long patterns from databases. In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pages 85-93, 1998.
[13] R.J. Bayardo and R. Agrawal. Mining the most interesting rules. In Proceedings of the 1999 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 145-154, 1999.
[14] R.J. Bayardo, R. Agrawal, and D. Gunopulos. Constraint-based rule mining in large, dense databases. In Proceedings of the 1999 IEEE International Conference on Data Engineering, pages 188-197, 1999.
[15] C.J. Moran B.L. Henderson, E.N. Bui and D.A.P. Simon. Australiawide predictions of soil properties using decision trees. Geoderma, 124(3-4):383-398, 2005.
[16] S. Brin, R. Motwani, J. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, pages 255-264, 1997.
[17] D. Burdick, M. Calimlim, and J. Gehrke. Mafia: A maximal frequent itemset algorithm for transactional databases. In Proceedings of the 2001 IEEE International Conference on Data Engineering, pages 443-552, 2001.
[18] K.C.C. Chan and W-H. Au. Mining fuzzy association rules. In Proceedings of the 1997 International Conference on Information and Knowledge Management, pages 209-215, 1997.
[19] Liu C-H. Wang Y-W. Chang, P-C. A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry. Decisions Support Systems, Available online 13 December 2005.
[20] G. Chen and Q. Wei. Fuzzy association rules and the extended mining algorithms. Information Sciences, 147:201-228, 2002.
[21] G. Chen, Q. Wei, and E. Kerre. Fuzzy data mining: Discovery of fuzzy generalized association rules. In Recent Issues on Fuzzy Databases, pages 45-66. Springer, 2000.
[22] Henri Prade Didier Dubois. What are fuzzy rules and how to use them. Fuzzy Sets and Systems, 84:169-185, 1996.
[23] Zhang X. Wong-L. Li J. Dong, G. Caep: classification by aggregating emerging patterns. In Second International Conference on Discovery Science, 1999.
[24] J. Abonyi F. D. Tamas, F. P. Pach and A. M. Esteves. Analysisof trace elements in clinker based on supervised clustering and fuzzy decision tree induction. In 6th International Congress, Global Construction: Ultimate Concrete Opportunities, Dundee, Scotland, 2005.
[25] P. Arva F. P. Pach, A. Gyenesei and J. Abonyi. Fuzzy association rule mining for model structure identification. In 10th Online World Conference on Soft Computing in Industrial Application, WSC10, 2005.
[26] P. Arva F. P. Pach, A. Gyenesei and J. Abonyi. Fuzzy association rule mining for model structure identification. In Applications of Soft Computing: Recent Trends, Springer, 2006, In Press.
[27] S. Nemeth P. Arva J. Abonyi F. P. Pach, A. Gyenesei. Fuzzy association rule mining for the analysis of historical process data. Acta Agraria Kaposvariensis, 2006, In Press.
[28] S. Nemeth P. Arva J. Abonyi F. P. Pach, F. Szeifert. Fuzzy association rule mining for data-driven analysis of dynamical systems. Hungarian Journal of Industrial Chemistry, Special Issue on Recent advances in Computer Aided Process Engineering, 2006, In Press.
[29] S. Nemeth P. Arva F.P. Pach, J. Abonyi. Supervised clustering and fuzzy decision tree induction for the identification of compact classifiers. In 5th International Symposium of Hungarian Researchers on Computational Intelligence, Budapest, Hungary, 2004.
[30] Paul Leng Frans Coenen. The effect of threshold values on association rule based classification accuracy. Data and Knowledge Engineering, Available online, 2006.
[31] I. Gath and A.B. Geva. Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:773-781, 1989.
[32] Pal N.R. Das J. Ghosh, A. A fuzzy rule based approach to cloud cover estimation. Remote Sensing of Environment, 100:531-549, 2006.
[33] D.E. Gustafson and W.C. Kessel. Fuzzy clustering with fuzzy covariance matrix. In In Proceedings of the IEEE CDC, San Diego, pages 761-766, 1979.
[34] S. Kper J. Zhang and A. Knoll. Extracting compact fuzzy rules based on adaptive data approximation using b-splines. Information Sciences, 142(1-4):227-248, 2002.
[35] C.Z. Janikow. Fuzzy decision trees: issues and methods. IEEE Trans. Systems Man Cybernet. Part B (Cybernetics), 28(1):1-14, 1998.
[36] C.Z. Janikow. Fuzzy partitioning with fid 3.1. In Proc. 18th Internat. Conf. of the North American Fuzzy Information Processing Society, NAFIPS99, pages 467-471, 1999.
[37] Patrick Soriano Jean-Yves Potvin and Maxime Valle. Generating trading rules on the stock markets with genetic programming. Computers and Operations Research, 31(7):1033-1047, 2004.
[38] E.S. Karapidakis. Machine learning for frequency estimation of power systems. Applied Soft Computing, In Press, Corrected Proof, Available online 28 December 2005.
[39] Kun Chang Lee and Sung Joo Park. A knowledge-based fuzzy decision tree classifier for time series modeling. Fuzzy Sets and Systems, 33(1):1- 18, 1989.
[40] Ricardo Linden and Amit Bhaya. Evolving fuzzy rules to model gene expression. Biosystems, In Press, Accepted Manuscript, Available online 30 April 2006.
[41] Ma Y. Liu, B. and Wong C. K. Improving an association rule based classifier. In Principles of Data Mining and Knowledge Discovery, pages 504-509, 2000.
[42] T. Bar-Noy M. Friedman and M. Blau A. Kandel. Certain computational aspects of fuzzy decision trees. Fuzzy Sets and Systems, 28(2):163-170, 1988.
[43] Bing Liu Wynne Hsu Yiming Ma. Integrating classification and association rule mining. In Appeared in KDD-98, New York, 1998.
[44] D. Meretakis and B. Wuthrich. Extending naive bayes classifiers using long itemsets. In Knowledge Discovery and Data Mining, pages 165- 174, 1999.
[45] A. Keith Dunker Predrag Radivojac, Nitesh V. Chawla and Zoran Obradovic. Classification and knowledge discovery in protein databases. Journal of Biomedical Informatics, 37(4):224-239, 2004.
[46] J. R. Quinlan. Induction on decision trees. Machine Learning, 1(1):81- 106, 1986.
[47] J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
[48] R. Agrawal R. Srikant. Mining generalized association rules. In The Internat. Conf. on Very Large Databases, 1995.
[49] A. Abuelgasim R.H. Fraser and R. Latifovic. A method for detecting large-scale forest cover change using coarse spatial resolution imagery. Remote Sensing of Environment, 95(4):414-427, 2005.
[50] Sbastien Thomassey and Antonio Fiordaliso. A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems, In Press, Corrected Proof,, Available online 30 March 2005.
[51] Kuei-Ying Lin Tzung-Pei Hong and Shyue-Liang Wang. Fuzzy data mining for interesting generalized association rules. Fuzzy Sets and Systems, 138(2):255-269, 2003.
[52] J. M. Zurada W. Duch, R. Setiono. Computational intelligence methods for rule-based data understanding. Proc. of the IEEE, 92(5), 2004.
[53] Zhou S. Wang, K. and Y. He. Growing decision tree on support-less association rules. In In proceedings of KDD-00, Boston, MA, 2000.
[54] R. Weber. Fuzzy id3: a class of methods for automatic knowledge acquisition. In Proc. 2nd Internat. Conf. on Fuzzy Logic and Neural Networks, Iizuka, Japan, page 265268, 1992.
[55] Zenon A. Sosnowskic Witold Pedrycz. The design of decision trees in the framework of granular data and their application to software quality models. Fuzzy Sets and Systems, 123:271290, 2001.
[56] Gwo-Hshiung Tzeng Yi-Chung Hu. Elicitation of classification rules by fuzzy data mining. Engineering Applications of Artificial Intelligence, 16:709716, 2003.
[57] Gwo-Hshiung Tzeng Yi-Chung Hu, Ruey-Shun Chen. Mining fuzzy association rules for classification problems. Computers and Industrial Engineering, 43:735750, 2002.
[58] X. Yin and J. Han. Cpar: Classification based on predictive association rules. In in Proceedings of 2003 SIAM International Conference on Data Mining (SDM-03), 2003.
[59] A. Zimmermann and Raedt L. D. Corclass: Correlated association rule mining for classification. In Discovery Science, 7th International Conference, Padova, Italy, pages 60-72, 2004.