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2001 | 11 | 3 | 655-674
Tytuł artykułu

Concept approximations based on rough sets and similarity measures

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The formal concept analysis gives a mathematical definition of a formal concept. However, in many real-life applications, the problem under investigation cannot be described by formal concepts. Such concepts are called the non-definable concepts (Saquer and Deogun, 2000a). The process of finding formal concepts that best describe non-definable concepts is called the concept approximation. In this paper, we present two different approaches to the concept approximation. The first approach is based on rough set theory while the other is based on a similarity measure. We present algorithms for the two approaches.
Rocznik
Tom
11
Numer
3
Strony
655-674
Opis fizyczny
Daty
wydano
2001
otrzymano
2001-03-01
poprawiono
2001-06-01
Twórcy
autor
  • Computer Science Department, Southwest Missouri State University, Springfield, MO 65804, USA
  • Computer Science and Engineering Department, University of Nebraska, Lincoln, NE 68588, USA
Bibliografia
  • Carpineto C. and Romano G. (1996): A lattice conceptual clustering system and its application to browsing retrieval. - Mach. Learn., Vol.24, No.2, pp.95-122.
  • Ganter B. (1984): Two basic algorithms in concept analysis. - FB4-Preprint No. 831, TH Darmstadt.
  • Ganter B. and Wille R. (1999): Formal Concept Analysis: Mathematical Foundations. - Berlin: Springer.
  • Godin R. and Missaoui R. (1994): An incremental concept formation for learning from databases. - Theoret. Comp. Sci., Vol.133, No.2, pp.387-419.
  • Ho T.B. (1995): An approach to concept formation based on Formal Concept Analysis. - IEICE Trans. Inform. Syst., Vol.78, No.5, pp.553-559.
  • Kangassalo H. (1992): On the concept of concept for conceptualmodeling and concept deduction, In: Information Modeling and Knowledge Bases III (S. Ohsuga, H. Kangassalo and H. Jaakkola, Eds.). - Amsterdam: IOS Press, pp.17-58.
  • Kent R. (1994): Rough concept analysis. - Proc. Int. Workshop Rough Sets and Knowledge Discovery, Banff, Canada, pp.245-253.
  • Kent R. (1996): Rough concept analysis: A synthesis of roughsets and formal concept analysis. - Fund. Inform., Vol.27, No.2, pp.169-181.
  • Pawlak Z. (1982): Rough sets. - Int. J. Inf. Comp. Sci., Vol.11, No.5, pp.341-356.
  • Saquer J. and Deogun J. (1999): Formal rough concept analysis, In: New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (N. Zhong, A. Skowron and S. Ohsuga, Eds.). - Yamaguchi, Japan: Springer, pp.91-99.
  • Saquer J. and Deogun J. (2000a): Concept approximations for formal concept analysis. - Proc. 8-th Int. Conf. Conceptual Structures (ICCS'2000), Vol.II, Working with Conceptual Structures, Darmstadt, Germany, pp.73-83.
  • Saquer J. and Deogun J. (2000b): Using closed itemsets for discovering representative association rules, In:Proc. 12-th Int. Symp. Methodologies for Intelligent Systems (ISMIS'2000) (Z. Ras and S. Ohsuga, Eds.). - Charlotte, NC: Springer, pp.495-504.
  • Wille R. (1992): Concept lattices and conceptual knowledge systems. - Comp. Math. Appl., Vol.23., No.5, pp.493-515.
  • Wille R. (1982): Restructuring lattice theory: An approach based on hierarchies on concepts, In: Ordered Sets (I. Rival, Ed.). - Dordrecht: Reidel, pp.445-470.
  • Wille R. (1989): Knowledge acquisition by methods of formal concept analysis, In: Data Analysis, Learning Symbolic and Numeric Knowledge (E. Diday, Ed.). - New York: Nova Science,pp.365-380.
  • Zadeh L. (1965): Fuzzy sets. - Inform. Contr., Vol.8, pp.338-353.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv11i3p655bwm
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