Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications

International Journal of Data Mining and Bioinformatics

Volumn 1, Issue 1, 2006, Pages 3-18

  He, Yuanchen ^a   Tang, Yuchun ^a   Zhang, Yan Qing ^a   Sunderraman, Rajshekhar ^a  

^a GEORGIA STATE UNIVERSITY   (United States)

Author keywords

binary classification; bioinformatics; data mining; Decision Support System (DSS); Fuzzy Association Rules (FARs); knowledge discovery; medical informatics; Support Vector Machines (SVMs)

Indexed keywords

ALGORITHM; ARTICLE; DECISION THEORY; FACTUAL DATABASE; FUZZY LOGIC; HUMAN; MEDICAL RESEARCH;

ALGORITHMS; BIOMEDICAL RESEARCH; DATABASES, FACTUAL; DECISION THEORY; FUZZY LOGIC; HUMANS;

EID: 33750569049     PISSN: 17485673     EISSN: 17485681     Source Type: Journal    
DOI: 10.1504/IJDMB.2006.009919     Document Type: Article

References (32)

1. Agrawal, R. and Srikant, R. (1994) ‘Fast algorithms for mining association rules’, Proc. Int. Conf. Very Large Data Bases (VLdB’94), Santiago, Chile, September, pp.487–499.
2. Agrawal, R., Imielinski, T. and Swami, A. (1993) ‘Mining association rules between sets of items in large databases’, Proc. ACM-SIgMoD Int. Conf. Management of Data (SIGMOD’93), Washington, DC, July, pp.45-51.
3. Au, W.H. and Chan, K.C.C. (2003) ‘Mining fuzzy association rules in a bank-account database’, IEEE Transactions on Fuzzy Systems, Vol. 11, No. 2, pp.238–248.
4. Bennett, K.P. and Blue, J. (1997) A Support Vector Machine Approach to Decision Trees, R.P.I Math Report No. 97-100, Rensselaer Polytechnic Institute, Troy, NY.
5. Chan, K.C.C. and Au, W.H. (1997) ‘Mining fuzzy association rules’, Proc. 6th Int. Conf. Information Knowledge Management, Las Vegas, NV, pp.209–215.
6. Chatterjee, A. and Rakshit, A. (2004) ‘Influential Rule Search Scheme (IRSS): a new fuzzy pattern classifier’, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 8, pp.881-893.
7. Delgado, M., Marin, N., Martin-Bautista, M.J., Sanchez, D. and Vila, M.A. (2003a) ‘Mining fuzzy association rules: an overview’, BISC International Workshop on Soft Computing for Internet and Bioinformatics.
8. Delgado, M., Marin, N., Sanchez, D. and Vila, M.A. (2003b) ‘Fuzzy association rules: general model and applications’, IEEE Transactions on Fuzzy Systems, Vol. 11, No. 2, pp.214–225.
9. Dick, S. and Kandel, A. (1999) ‘Combinatorial rule explosion eliminated by a fuzzy rule configuration’, IEEE Transactions on Fuzzy Systems, Vol. 7, pp.475–477.
10. Duch, W., Adamczak, R. and Grabczewski, K. (2001) ‘A new methodology of extraction, optimization and application of crisp and fuzzy logical rules’, IEEE Transactions on Neural Networks, Vol. 12, pp.277-306.
11. Hand, D., Mannila, H. and Smyth, P. (2001) Principle of Data Mining, The MIT Press, London.
12. Hu, Y.C., Chen, R.S. and Tzeng, G.H. (2002) ‘Mining fuzzy association rules for classification problem’, Computers and Industrial Engineering, Vol. 43, No. 4, pp.735–750.
13. Ishibuchi, H., Nakashima, T. and Murata, T. (1999) ‘Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems’, IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 29, No. 5, pp.353–361.
14. Jang, J.S.R. (1995) ‘ANFIS: Adaptive-Network-based Fuzzy Inference System’, IEEE Transactions on Systems, Man and Cybernetics, Vol. 23, No. 3, pp.665–685.
15. Janikow, C.Z. (1998) ‘Fuzzy decision trees: issues and methods’, IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 28, No. 1, pp.1–14.
16. Kaufman, L. and Rousseeuw, P.J. (1990) Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley and Sons, Inc., New York.
17. Kruse, R. and Nauck, D. (1995) ‘NEFCLASS: a neuro-fuzzy approach for the classification of data’, Proc. the 1995 ACM Symposium on Applied Computing, ACM Press, New York, pp.461–465.
18. Li, W., Han, J. and Pei, J. (2001) ‘CMAR: accurate and efficient classification based on multiple class-association rules’, Proc. ICDM2001, pp.369–376.
19. Luo, J. and Bridges, S.M. (2000) ‘Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection’, International Journal of Intelligent Systems, Vol. 15, No. 8, pp.687–704.
20. Mangasarian, O.L., Street, W.N. and Wolberg, W.H. (1994) Breast Cancer Diagnosis and Prognosis via Linear Programming, Mathematical Programming Technical Report 94–10, University of Wisconsin.
21. Merz, C.J. and Murphy, P.M. (1998) UCI Repository of Machine Learning Databases, Available from: http://www.ics.uci.edu/~mlearn/MLRepository.html.
22. Muselli, M. and Liberati, D. (2002) ‘Binary rule generation via Hamming clustering’, IEEE Transactions on Knowledge and Data Engineering, Vol. 14, pp.1258–1268.
23. Pena-Reyes, C.A. and Sipper, M. (2000) ‘Applying fuzzy coco to breast cancer diagnosis’, Proc. of the 2000 Congress on Evolutionary Computation (CEC00), IEEE Press, Piscataway, NJ, pp.1168–1175.
24. Sugeno, M. and Kang, G.T. (1988) ‘Structure identification of fuzzy model’, Fuzzy Sets System, Vol. 28, pp.15–33.
25. Takagi, T. and Sugeno, M. (1985) ‘Fuzzy identification of systems and its applications to modeling and control’, IEEE Transactions on Systems, Man and Cybernetics, Vol. 15, pp.116—132.
26. Tang, Y.C., Jin, B., Sun, Y. and Zhang, Y.Q. (2004) ‘Granular support vector machines for medical binary classification problems’, Proc. IEEE CIBIB 2004, October 7–8, San Diego.
27. Uebele, V., Abe, S. and Lan, M.S. (1995) ‘A neural-network-based fuzzy classifier’, IEEE Transactions on Systems, Man and Cybernetics, Vol. 25, No. 2, pp.353–361.
28. Wang, X.Z., Yeung, D.S. and Tsang, E.C.C. (2001) ‘A comparative study on heuristic algorithms for generating fuzzy decision trees’, IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 31, No. 2, pp.215–226.
29. Wong, S.S.C. and Pal, S. (2001) ‘Mining fuzzy association rules for web access case adaptation’, Workshop on Soft Computing in Case-Based Reasoning, International Conference on Case-Based Reasoning (ICCBR’01).
30. Yin, X. and Han, J. (2003) ‘CPAR: classification based on predictive association rules’, Proc. SIAMInt. Conf. on Data Mining (SDM’03), San Francisco, CA, pp.331–335.
31. Zadeh, L. (1965) ‘Fuzzy sets’, Journal of Information and Control, Vol. 8, pp.338–353.
32. Zadeh, L. (1973) ‘Outline of a new approach to the analysis of complex systems and decisions processes’, IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, No. 1, pp.28–44.