Rule Extraction Using Rough Sets When Membership Values Are Intervals

As corporate data is more and more becoming an invaluable asset for decision makers, technologies such as data warehouses and data mining are an essential part of a company's information infrastructure. Methods for mining coiporate data and extracting rules from it are well established for crisp and traditional fuzzy data. This paper aims at extending the traditional fuzzy approach for situations where membership values are intervals.

[1]  Robert M. Kleyle,et al.  An evidential approach to problem solving when a large number of knowledge systems is available , 1990, Int. J. Intell. Syst..

[2]  P Cheeseman,et al.  Induction of models under uncertainty , 1986, ISMIS '86.

[3]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[4]  Douglas Graham,et al.  Building the Corporate Intranet , 1996 .

[5]  Ronald R. Yager,et al.  Approximate reasoning as a basis for rule-based expert systems , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Roman Słowiński,et al.  The application of rough sets theory to the verification of indications for treatment of duodenal ulcer by HSV , 1987 .

[7]  Z. Pawlak.  Rough sets and fuzzy sets , 1985 .

[8]  Daniel G. Bobrow,et al.  Expert systems: perils and promise , 1986, CACM.

[9]  Lotfi A. Zadeh,et al.  Possibility theory and soft data analysis , 1996 .

[10]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[11]  Ryan Bernard.  The corporate Intranet , 1997 .

[12]  R Blum,et al.  Acquisition of knowledge from data , 1986, ISMIS '86.

[13]  Zdzisław Pawlak,et al.  Rough sets. Basic notions , 1981 .

[14]  Zdzislaw Pawlak,et al.  Rough classification , 1984, Int. J. Hum. Comput. Stud..

[15]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[16]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[17]  Tom Hammergren.  Data warehousing , 1996 .

[18]  W Ziarko,et al.  Adaptative expert system for preliminary engineering design , 1987 .

[19]  Robert Groth,et al.  Data Mining , 1998 .

[20]  Thomas M. Strat,et al.  Decision analysis using belief functions , 1990, Int. J. Approx. Reason..

[21]  Robert M. Kleyle,et al.  A unified model for data acquisition and decision making , 1989, J. Inf. Sci..

[22]  K HirjiKarim.  Discovering data mining , 1999 .

[23]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[24]  Z. Pawlak.  Classification of objects by means of attributes , 1981 .

[25]  Jerzy W. Grzymala-Busse,et al.  Knowledge acquisition under uncertainty — a rough set approach , 1988, J. Intell. Robotic Syst..