Commenced in January 2007
Paper Count: 30184
Discovery of Fuzzy Censored Production Rules from Large Set of Discovered Fuzzy if then Rules
Abstract:Censored Production Rule is an extension of standard production rule, which is concerned with problems of reasoning with incomplete information, subject to resource constraints and problem of reasoning efficiently with exceptions. A CPR has a form: IF A (Condition) THEN B (Action) UNLESS C (Censor), Where C is the exception condition. Fuzzy CPR are obtained by augmenting ordinary fuzzy production rule “If X is A then Y is B with an exception condition and are written in the form “If X is A then Y is B Unless Z is C. Such rules are employed in situation in which the fuzzy conditional statement “If X is A then Y is B" holds frequently and the exception condition “Z is C" holds rarely. Thus “If X is A then Y is B" part of the fuzzy CPR express important information while the unless part acts only as a switch that changes the polarity of “Y is B" to “Y is not B" when the assertion “Z is C" holds. The proposed approach is an attempt to discover fuzzy censored production rules from set of discovered fuzzy if then rules in the form: A(X) ÔçÆ B(Y) || C(Z).
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072836Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1133
 R. Agrawal, T. Lmielinski, and A.Swami, "Fast Algorithms for Mining Association Rules". In Proc. Of the 20 th International Conference on VLDB, Santiago, Chiles. May 1993, pp.487-499.
 A. Gonazalez and R. Perez, " Structural Learning of Fuzzy Rules from Noised examples", Fourth IEEE International Conference on Fuzzy systems, Yokohama, japan, Vol. 3, pp. 1323 - 1330, 1995.
 Neerja and K.K. Bharadwaj " Calculus of Fuzzy Hierarchical Censored Production Rules (FHCPRs)" International Journal of Intelligent Systems Vol. 11, pp. 1- 25, 1996.
 U. Fayyad, G.P. Shapiro and P. Smyth, "The KDD Process for extracting Useful Knowledge from Volumes of Data". Communication of ACM. 39(11), Nov, 1996. pp. 27-34.
 J.L. Castro and J.M. Zurita, " An Inductive Learning Algorithm in Fuzzy Systems", ACM, Vol. 89, pp. 193 - 203, 1997.
 F. Hussain, H.Liu, E.Suzuki and H. Lu, "Exception Rule Mining with a Relative Interestingness Measure". Knowledge Discovery and Data Mining. Lecture Notes in Artificial Intelligence 1805 (PAKDD), T. Terano, H. Liu and A.L.P. Chen (eds.), Kyoto, Japan. Springer- Verlag. April 2000, pp. 86-97.
 N. K. Jain, K. K. Bharadwaj and N. Marranghello, "Extended of Hierarchical Censored Production Rules (HCPRs) Systems: Ann approach toward Generalized Knowledge Representation". Journal of Intelligent Systems, 9(3,4), 1999, pp. 259-295.
 J. Kivien, H. Mannila, and E. Ukkonen, "Learning rules with local exceptions". EuroCOLT-93. 1994, pp.53, 35-46.
 B.Liu, M. Hu and W.Hsu, "Intuitive Representation of Decision Trees Using General Rules and Exceptions". AAAI-2000.
 R.S. Michalski and P.H. Winston, "Variable Precision Logic". Artificial Intelligence Journal, 29, Elsevier Science Publisher, B.V. (North- Holland). 1986, pp. 121-146.
 E. Suzuki and J.M Zytkow, "Unified Algorithm for Undirected Discovery of Exception Rules". PKDD-2000, pp. 169-180.
 E. Suzuki, "Mining Financial Data with Scheduled Discovery of Exception Rules". Principles of Data Mining and Knowledge Discovery. Lecture Notes in artificial intelligence 1910 (PKDD), Springer- Verlag, D.A. Zighed, J. Komorowski, J.M. Zytkow (eds.), Lyon, France. September 2000, pp. 169-180.
 M.R. Tolun, H. Sever and M. Uludag, "Improved Rules Discovery Performance on Uncertainty". PAKDD-98, Melbourne, Australia. April, 1998, pp. 15-17.
 M.R. Tolun and S. Abu-Soud, "ILA: An Inductive Learning Algorithm for Production Rule Discovery". 14(3),April 1998, pp. 361-370.
 H. Liu and H. Lu, "Efficient search of reliable exceptions". In proc. Third Pacific- Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 1999, pp. 194-203.
 H. Mannila, M. Klemettien, "Finding Interesting rules from large sets of discovered association rules". In third International Conference on Information and Knowledge Management (CIKM) 1994.
 P. Smyth and R. M. Goodman, "Rule Induction using Information Theory". In Knowledge Discovery in Databases, Piatetsky- Shapiro, G. AAAI Press/ The MIT Pres, 1991, pp. 159-176.
 E. Suzuki, "Discovering unexpected exceptions: A stochastic approach". In Proc. RFID, 1996, pp.225-232.
 E. Suzuki, "Autonomous Discovery of Reliable Exception Rules". In Proc. Third International Conference on Knowledge Discovery and Data Mining (KDD), 1997, pp. 259-262.
 Dimiter Driankov and Hans Hellendon, "Fuzzy Logic With Unless- Rules", Proceeding of IEEE International Conference on Fuzzy Systems, 255-262, 1992.
 N. K. Jain & K. K. Bharadwaj, "Some learning Techniques in Hierarchical Censored Production Rules (HCPRs) Systems". International Journal of Intelligent Systems, 13, 1998, pp.319-344.
 Tamanna Siddiqui, K. K. bharadwaj, "Discovery of Quantified Censored Production Rules from the Large set of Discovered rules", Proceedings of International Conference: Conference on Information Science, Technology and Management (CISTM 2006), Chandigarh, India. July 16-18, 2006.