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2001 | 11 | 3 | 621-635
Tytuł artykułu

Rough relation properties

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. This paper rewrites some properties of rough relations found in the literature, proving their validity.
Rocznik
Tom
11
Numer
3
Strony
621-635
Opis fizyczny
Daty
wydano
2001
otrzymano
2001-03-01
poprawiono
2001-06-01
Twórcy
  • Universidade Federal de Sao Carlos, Departamento de Computacao, Sao Carlos-Sao Paulo, Brazil
  • Universidade Federal de Lavras, Departamento de Ciencia da Computacao, Lavras-Minas Gerais, Brazil
  • Universidade Federal de Sao Carlos, Departamento de Matematica, Sao Carlos-Sao Paulo, Brazil
Bibliografia
  • Aasheim O.T. and Solheim H.G. (1996): Rough set as a framework for data mining. — Project Report of the Knowledge Systems Group, Faculty of Computer Systems and Telematics, Norwegian University of Science and Technology, Trondheim, Norway, p.147.
  • Berztiss A.T. (1975): Data Structures — Theory and Practice, 2nd Ed. — New York: Academic Press.
  • Deogun J.S., Raghavan V.V., Sarkar A. and Sever H. (1997): Data mining: Research trends, challenges, and applications, In: Rough Sets and Data Mining: Analysis of Imprecise Data (T.Y. Lin and N. Cercone, Eds.). — Boston: Kluwer Academic, pp.9–45.
  • Grzymala-Busse J.W. (1986): On the reduction of knowledge representation systems, Proc. 6th Int. Workshop Expert Systems and Their Applications, Avignon, France, Vol.1, pp.463–478.
  • Grzymala-Busse J.W. (1988): Knowledge acquisition under uncertainty — A rough set approach. — J. Intell. Robot. Syst., Vol.1, No.1, pp.3–16.
  • Grzymala-Busse J.W. (1992): LERS — A system for learning from examples based on rough sets, In: Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory. — Dordrecht: Kluwer, pp.3–18.
  • Grzymala-Busse J.W. and Mithal S. (1991): On the choice of the best test for attribute dependency in programs for learning from examples. — Int. J. Softw. Eng. Knowl. Eng., Vol.1, No.4, pp.413–438.
  • Jelonek J., Krawiec K. and Slowinski R. (1994): Rough set reduction of attributes and their domains for neural networks. — Comput. Intell., Vol.2, No.5, pp.1–10.
  • Klir G.J. and Yuan B. (1995): Fuzzy Sets and Fuzzy Logic — Theory and Applications. — Upper Saddle River: Prentice Hall.
  • Mrózek A. (1989): Rough sets and dependency analysis among attributes in computer implementations of expert inference models. — Int. J. Man-Machine Stud., Vol.30, pp.457–473.
  • Mrózek A. (1992): A new method for discovering rules from examples in expert systems. — Int. J. Man-Machine Stud., Vol.36, pp.127–143.
  • Nicoletti M.C. and Uchoa J.Q. (1997): The use of membership functions for characterizing the main concepts of rough set theory. — Techn. Rep. No.005/97, Departamento de Computacao-Universidade Federal de Sao Carlos, p. 26, (in Portugese).
  • Ohrn A. (1993): Rough logic control: A new approach to automatic control? — Techn. Rep., Trondheim University, Norway, p.44.
  • Orlowska E. and Pawlak Z. (1984): Expressive power of knowledge representation systems. — Int. J. Man-Machine Stud., Vol.20, pp.485–500.
  • Pawlak Z. (1981): Rough relations. — Techn. Rep. No.435, Institute of Computer Science, Polish Academy of Sciences, Warsaw.
  • Pawlak Z. (1982): Rough sets. — Int. J. Inf. Comp. Sci., Vol.11, No.5, pp.341–356.
  • Pawlak Z. (1984): Rough classification. — Int. J. Man-Machine Stud., Vol.20, pp.469–483.
  • Pawlak Z. (1985): On Learning — A Rough Set Approach, In: Lecture Notes in Computer Science (A. Skowron, Ed.). — Berlin: Springer, Vol.28, pp.197–227.
  • Pawlak Z. (1994): Hard and soft sets, In: Rough Sets, Fuzzy Sets and Knowledge Discovery (W.P. Ziarko, Ed.). — Berlin: Springer, pp.130–135.
  • Pawlak Z. (1997): Rough real functions and rough controllers, In: Rough Sets and Data Mining: Analysis for Imprecise Data (T.Y. Lin and N. Cercone, Eds.). — Boston: Kluwer.
  • Pawlak Z. and Skowron R. (1994): Rough membership functions, In: Advances in the Dempster-Shafer Theory of Evidence (R. Yager, M. Fedrizzi and J. Kacprzyk, Eds.). — New York: Wiley, pp.251–271.
  • Pawlak Z., Wong S.K.M. and Ziarko W. (1988): Rough sets: Probabilistic versus deterministic approach. — Int. J. Man-Machine Stud., Vol.29, pp.81–95.
  • Skowron A. and Grzymala-Busse J. (1994): From rough set theory to evidence theory, In: Advances in the Dempster-Shafer Theory of Evidence (R. Yager, M. Fedrizzi and J. Kacprzyk, Eds.). — New York: Wiley, pp.193–236.
  • Słowiński R. (1995): Rough set approach to decision analysis. — AI Expert (March), pp.19–25.
  • Szladow A. and Ziarko W. (1993): Rough sets: Working with imperfect data. — AI Expert (July), pp.36–41.
  • Wong S.K.M. and Lingras P. (1989): The compatibility view of Dempster-Shafer theory using the concept of rough set. — Proc. 4th Int. Symp. for Intelligent Systems, Charlotte, Vol.4, pp.33–42.
  • Wong S.K.M., Ziarko W. and Ye R.L. (1986): Comparison of rough set and statistical methods in inductive learning. — Int. J. Man-Machine Stud., Vol.24, pp.53–72.
  • Wygralak M. (1989): Rough sets and fuzzy sets — Some remarks on interrelations. — Fuzzy Sets Syst., Vol.29, pp.241–243.
  • Ziarko W. (1991): The discovery, analysis, and representation of data dependencies in databases, In: Knowledge Discovery in Databases (G. Piatestsky-Shapiro and W. Frawley, Eds.). — Menlo Park: AAI Press/MIT Press, pp.195–209.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv11i3p621bwm
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