Fuzzy Miner: Extracting Fuzzy Rules from Numerical Patterns

International Journal of Data Warehousing and Mining (IJDWM)

Volumn 1, Issue 1, 2005, Pages 57-81

  Pelekis, Nikos ^a,b   Theodoulikis, Babis ^b   Kopanakis, Ioannis ^b   Theodoridis, Yannis ^a  

^a UNIVERSITY OF PIRAEUS   (Greece)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

data mining; fuzzy logic; fuzzy rules; numerical patterns; pattern classification

Indexed keywords

EID: 85001716972     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2005010103     Document Type: Article

References (32)

1. Aamodt, A., & Plazas, E. (1994). Casebased reasoning: Foundational issues, methodological variations, and system approaches. AI Comm., 7, 39–52.
2. ASE (2004). The Athens Stock Exchange closing prices. Retrieved March 26, 2004: http://www.ase.gr/content/en/MarketData/Stocks/Prices/default.asp
3. Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis (second edition). Springer-Verlag.
4. Bigus, J.P. (1996). Data Mining with Neural Networks: Solving Business Problems. McGraw-Hill.
5. Chen, M.S., Han, J., & Yu, P.S. (1996). Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866–883.
6. Cios, K., Pedrycz, W., & Swiniarski, R. (1998). Data Mining Methods for Knowledge Discovery. Kluwer Academic Publishers.
7. Craven, M.W. & Shavlik, J.W. (1997). Using neural networks in data mining. Future Generation Computer Systems, 13, 211–229.
8. Han, J. & Kamber, M. (2001). Data Mining: Concepts and Techniques. Morgan Kaufmann.
9. Ichihashi, H. & Watanabe, T. (1990). Learning control system by a simplified fuzzy reasoning model. Proceedings of IPMU'90, (pp. 417–419).
10. Kopanakis, I. & Theodoulidis, B. (2003). Visual data mining modeling techniques for the visualization of mining outcomes. Journal of Visual Languages and Computing, Special Issue on Visual Data Mining, 14(6), 543–589.
11. Kosko, B. (1992). Fuzzy systems as universal approximators. Proceedings of FUZZ-IEEE '92, (pp. 1153–1162).
12. Leake, D.B. (1996). CBR in context: The present and future. In D.B. Leake (Ed.), Case-Based Reasoning: Experience, Lessons and Future Directions (pp. 3–30). AAAI Press.
13. Lenarcik, A. & Piasta, Z. (1997). Probabilistic rough classifiers with mixture of discrete and continuous variables. In T.Y. Lin & N. Cercone (Eds.), Rough Sets and Data Mining: Analysis for Imprecise Data (pp. 373–383). Kluwer Academic Publishers.
14. Liu, B., Xia, Y., & Yu, P. (2000). Clustering through decision tree construction. Proceedings of ACM CIKM.
15. Manoranjan, V.S., Lazaro, A. de Sam, Edwards, D., & Aathalye (1995). A systematic approach to obtaining fuzzy sets for control systems. IEEE Transactions on Systems, Man and Cybernetics, 25(1).
16. Masters, T. (1993). Practical Neural Network Recipes in C++. Academic Press.
17. Mitchel, M. (1996). An Introduction to Genetic Algorithms. MIT Press.
18. Nauck, D. & Kruse, R. (1995). NEFCLASS- A Neuro-Fuzzy Approach for the Classification of Data. ACM Press.
19. Nomura, H., Hayashi, I., & Wakami, N. (1992). A learning method of fuzzy inference rules by descent method. In Proceedings of FUZZ-IEEE '92, (pp. 203–210).
20. Nozzaki, K., Ishibuchi, H., & Tanaka, H. (1997). A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems 86, 251–270.
21. Oja, E. (1983). Subspace Methods for Pattern Recognition. John Wiley.
22. Pawlak, Z. (1991). Rough Sets, Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers.
23. Pelekis, N. (1999). Fuzzy Miner: A fuzzy system for solving pattern classification problems. M.Sc. Thesis, UMIST. Retrieved June 15, 2004: http://users.forthnet.gr/ath/pele/HOME_PAGE_NIKOS_PELEKIS/Download/
24. Prabhu, N. (2003). Gauge groups and data classification. Applied mathematics and computation, 138(2–3), 267–289.
25. Schalkoff, R.J. (1992). Pattern Recognition: Statistical, Structural and NeuralApproaches. John Wiley.
26. Sugeno, M. & Kang, G.T. (1998). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28, 15–33.
27. Sugeno, M. & Yasukawa, T. (1993). A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Systems, 1, 7–31.
28. Swiniarski, R. (1998). Rough sets and principal component analysis and their applications in future extraction and selection, data model building and classification. In S. Pal & A. Skowron (Eds.),Fuzzy Sets, Rough Sets and Decision Making Processes. Springer-Verlag.
29. Takagi, H. & Hayashi, I. (1991). NNdriven fuzzy reasoning. Approximate reasoning, 5, 191–212.
30. Wang, L.X. (1992). Fuzzy systems as universal approximators. In Proceedings of FUZZ-IEEE'92, (pp. 1163–1170).
31. Wang, L.X. & Mendel, J.M. (1992). Generating fuzzy rules by learning from examples. IEEE Trans. Systems, Man Cybernet, 22, 1414–1427.
32. Zimmermann, H.-J. (1996). Fuzzy set theory and its applications (third edition).Kluwer Academic.