Discovery of Fuzzy Censored Production Rules from Large Set of Discovered Fuzzy if then Rules
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)  B(Y) || C(Z).
[1] 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.
[2] 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.
[3] Neerja and K.K. Bharadwaj " Calculus of Fuzzy Hierarchical Censored
Production Rules (FHCPRs)" International Journal of Intelligent
Systems Vol. 11, pp. 1- 25, 1996.
[4] 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.
[5] J.L. Castro and J.M. Zurita, " An Inductive Learning Algorithm in Fuzzy
Systems", ACM, Vol. 89, pp. 193 - 203, 1997.
[6] 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.
[7] 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.
[8] J. Kivien, H. Mannila, and E. Ukkonen, "Learning rules with local
exceptions". EuroCOLT-93. 1994, pp.53, 35-46.
[9] B.Liu, M. Hu and W.Hsu, "Intuitive Representation of Decision Trees
Using General Rules and Exceptions". AAAI-2000.
[10] 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.
[11] E. Suzuki and J.M Zytkow, "Unified Algorithm for Undirected
Discovery of Exception Rules". PKDD-2000, pp. 169-180.
[12] 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.
[13] M.R. Tolun, H. Sever and M. Uludag, "Improved Rules Discovery
Performance on Uncertainty". PAKDD-98, Melbourne, Australia. April,
1998, pp. 15-17.
[14] M.R. Tolun and S. Abu-Soud, "ILA: An Inductive Learning Algorithm
for Production Rule Discovery". 14(3),April 1998, pp. 361-370.
[15] 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.
[16] 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.
[17] 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.
[18] E. Suzuki, "Discovering unexpected exceptions: A stochastic approach".
In Proc. RFID, 1996, pp.225-232.
[19] E. Suzuki, "Autonomous Discovery of Reliable Exception Rules". In
Proc. Third International Conference on Knowledge Discovery and Data
Mining (KDD), 1997, pp. 259-262.
[20] Dimiter Driankov and Hans Hellendon, "Fuzzy Logic With Unless-
Rules", Proceeding of IEEE International Conference on Fuzzy Systems,
255-262, 1992.
[21] 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.
[22] 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.
[1] 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.
[2] 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.
[3] Neerja and K.K. Bharadwaj " Calculus of Fuzzy Hierarchical Censored
Production Rules (FHCPRs)" International Journal of Intelligent
Systems Vol. 11, pp. 1- 25, 1996.
[4] 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.
[5] J.L. Castro and J.M. Zurita, " An Inductive Learning Algorithm in Fuzzy
Systems", ACM, Vol. 89, pp. 193 - 203, 1997.
[6] 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.
[7] 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.
[8] J. Kivien, H. Mannila, and E. Ukkonen, "Learning rules with local
exceptions". EuroCOLT-93. 1994, pp.53, 35-46.
[9] B.Liu, M. Hu and W.Hsu, "Intuitive Representation of Decision Trees
Using General Rules and Exceptions". AAAI-2000.
[10] 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.
[11] E. Suzuki and J.M Zytkow, "Unified Algorithm for Undirected
Discovery of Exception Rules". PKDD-2000, pp. 169-180.
[12] 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.
[13] M.R. Tolun, H. Sever and M. Uludag, "Improved Rules Discovery
Performance on Uncertainty". PAKDD-98, Melbourne, Australia. April,
1998, pp. 15-17.
[14] M.R. Tolun and S. Abu-Soud, "ILA: An Inductive Learning Algorithm
for Production Rule Discovery". 14(3),April 1998, pp. 361-370.
[15] 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.
[16] 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.
[17] 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.
[18] E. Suzuki, "Discovering unexpected exceptions: A stochastic approach".
In Proc. RFID, 1996, pp.225-232.
[19] E. Suzuki, "Autonomous Discovery of Reliable Exception Rules". In
Proc. Third International Conference on Knowledge Discovery and Data
Mining (KDD), 1997, pp. 259-262.
[20] Dimiter Driankov and Hans Hellendon, "Fuzzy Logic With Unless-
Rules", Proceeding of IEEE International Conference on Fuzzy Systems,
255-262, 1992.
[21] 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.
[22] 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.
@article{"International Journal of Information, Control and Computer Sciences:58542", author = "Tamanna Siddiqui and M. Afshar Alam", title = "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)  B(Y) || C(Z).", keywords = "Uncertainty Quantification, Fuzzy if then rules,
Fuzzy Censored Production Rules, Learning algorithm.", volume = "3", number = "1", pages = "110-4", }
