This article outlines conceptualization and
implementation of an intelligent system capable of extracting
knowledge from databases. Use of hybridized features of both the
Rough and Fuzzy Set theory render the developed system flexibility
in dealing with discreet as well as continuous datasets. A raw data set
provided to the system, is initially transformed in a computer legible
format followed by pruning of the data set. The refined data set is
then processed through various Rough Set operators which enable
discovery of parameter relationships and interdependencies. The
discovered knowledge is automatically transformed into a rule base
expressed in Fuzzy terms. Two exemplary cancer repository datasets
(for Breast and Lung Cancer) have been used to test and implement
the proposed framework.
[1] Abidi. S. S. R, Cheah. Y-N, Curran J.; "A Knowledge Creation Info-
Structure to Acquire and Crystallize the Tacit Knowledge of Health-
Care Experts"; IEEE Trans. on Information Technology in
Biomedicine, v.9(2), pp. 193-204; 2005.
[2] Ohsuga. S; "Knowledge Discovery as Translation"; In Lin. T. Y et. al.
[3]; 2005.
[3] Lin T. Y, Ohsuga. S, Liau. C-J, Hu. X, Tsumoto. S; Foundations of Data
Mining and Knowledge Discovery; Springer-Verlag; Berlin; 2005.
[4] Hamilton-Wright. A, Stashuk. D. W; "Transparent Decision Support
Using Statistical Reasoning and Fuzzy Inference"; IEEE Trans. on
Knowledge and Data Engineering, v.18(8) , pp.1125-1137; 2006.
[5] Cao. L, "Domain-Driven, Actionable Knowledge Discovery"; IEEE
Intelligent Systems, v.22(4), pp.78-88; 2007.
[6] Pawlak. Z; "Some Issues on Rough Sets"; In Peters-Skowron [7]; 2004.
[7] Peters. J. F, Skowron. A; Transaction on Rough Sets-I; Springer; Berlin;
2004.
[8] Jang. J. S. R; "ANFIS: Adaptive Network Based Fuzzy Inference
Systems"; IEEE Transactions on Systems, Man and Cybernetics; May
1993.
[9] Jang. J. S. R, Sun. C. T, Mizutani. E; Neuro - Fuzzy and Soft
Computing: A Computational Approach to Learning and Machine
Intelligence; Prentice Hall; 1997.
[10] Demsar J, Zupan B; "Orange: From Experimental Machine Learning to
Interactive Data Mining"; White Paper (www.ailab.si/orange), Faculty
of Computer and Information Science, University of Ljubljana; 2004.
[11] Kudo. Y, Murai. T; "Missing Value Semantics and Absent Value
Semantics for Incomplete Information in Object-Oriented Rough Set
Models"; In Bello et. al. [13]; 2008.
[12] Jaganathan. P, Thangavel. K, Pethalakshmi. A, Karnan. M;
"Classification Rule Discovery with Ant Colony Optimization and
Improved Quick Reduct Algorithm"; Intl. J. of Computer Science,
v.33(1), pp.1-6; 2007.
[13] Bello. R, Falcon. R, Pedrycz. W, Kacprzyk. J; Granular Computing: At
the Junction of Rough Sets and Fuzzy Sets; Springer; London; 2008
[1] Abidi. S. S. R, Cheah. Y-N, Curran J.; "A Knowledge Creation Info-
Structure to Acquire and Crystallize the Tacit Knowledge of Health-
Care Experts"; IEEE Trans. on Information Technology in
Biomedicine, v.9(2), pp. 193-204; 2005.
[2] Ohsuga. S; "Knowledge Discovery as Translation"; In Lin. T. Y et. al.
[3]; 2005.
[3] Lin T. Y, Ohsuga. S, Liau. C-J, Hu. X, Tsumoto. S; Foundations of Data
Mining and Knowledge Discovery; Springer-Verlag; Berlin; 2005.
[4] Hamilton-Wright. A, Stashuk. D. W; "Transparent Decision Support
Using Statistical Reasoning and Fuzzy Inference"; IEEE Trans. on
Knowledge and Data Engineering, v.18(8) , pp.1125-1137; 2006.
[5] Cao. L, "Domain-Driven, Actionable Knowledge Discovery"; IEEE
Intelligent Systems, v.22(4), pp.78-88; 2007.
[6] Pawlak. Z; "Some Issues on Rough Sets"; In Peters-Skowron [7]; 2004.
[7] Peters. J. F, Skowron. A; Transaction on Rough Sets-I; Springer; Berlin;
2004.
[8] Jang. J. S. R; "ANFIS: Adaptive Network Based Fuzzy Inference
Systems"; IEEE Transactions on Systems, Man and Cybernetics; May
1993.
[9] Jang. J. S. R, Sun. C. T, Mizutani. E; Neuro - Fuzzy and Soft
Computing: A Computational Approach to Learning and Machine
Intelligence; Prentice Hall; 1997.
[10] Demsar J, Zupan B; "Orange: From Experimental Machine Learning to
Interactive Data Mining"; White Paper (www.ailab.si/orange), Faculty
of Computer and Information Science, University of Ljubljana; 2004.
[11] Kudo. Y, Murai. T; "Missing Value Semantics and Absent Value
Semantics for Incomplete Information in Object-Oriented Rough Set
Models"; In Bello et. al. [13]; 2008.
[12] Jaganathan. P, Thangavel. K, Pethalakshmi. A, Karnan. M;
"Classification Rule Discovery with Ant Colony Optimization and
Improved Quick Reduct Algorithm"; Intl. J. of Computer Science,
v.33(1), pp.1-6; 2007.
[13] Bello. R, Falcon. R, Pedrycz. W, Kacprzyk. J; Granular Computing: At
the Junction of Rough Sets and Fuzzy Sets; Springer; London; 2008
@article{"International Journal of Information, Control and Computer Sciences:49591", author = "Sandeep Chandana and Rene V. Mayorga and Christine W. Chan", title = "Automated Knowledge Engineering", abstract = "This article outlines conceptualization and
implementation of an intelligent system capable of extracting
knowledge from databases. Use of hybridized features of both the
Rough and Fuzzy Set theory render the developed system flexibility
in dealing with discreet as well as continuous datasets. A raw data set
provided to the system, is initially transformed in a computer legible
format followed by pruning of the data set. The refined data set is
then processed through various Rough Set operators which enable
discovery of parameter relationships and interdependencies. The
discovered knowledge is automatically transformed into a rule base
expressed in Fuzzy terms. Two exemplary cancer repository datasets
(for Breast and Lung Cancer) have been used to test and implement
the proposed framework.", keywords = "Knowledge Extraction, Fuzzy Sets, Rough Sets,
Neuro–Fuzzy Systems, Databases", volume = "2", number = "3", pages = "635-10", }