Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31442
Automated Knowledge Engineering

Authors: Sandeep Chandana, Rene V. Mayorga, Christine W. Chan


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

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1542


[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 (, 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