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International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing

RSFDGrC 1999: New Directions in Rough Sets, Data Mining, and Granular-Soft Computing pp 91–99Cite as

Formal Rough Concept Analysis

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 1711)

Abstract

In this paper, we present a novel approach for approximating concepts in the framework of formal concept analysis. Two main problems are investigated. The first, given a set A of objects (or a set B of features), we want to find a formal concept that approximates A (or B). The second, given a pair (A,B), where A is a set of objects and B is a set of features, the objective is to find formal concepts that approximate (A,B). The techniques developed in this paper use ideas from rough set theory. The approach we present is different and more general than existing approaches.

Keywords

  • Equivalence Class
  • Equivalence Relation
  • Lower Approximation
  • Concept Analysis
  • Formal Concept

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This research was supported in part by the Army Research O.ce, Grant No. DAAH04-96-1-0325, under DEPSCoR program of Advanced Research Projects Agency, Department of Defense and by the U.S. Department of Energy, Grant No. DE-FG02-9 7ER1220.

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References

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Author information

Authors and Affiliations

  1. The Department of Computer Science, University of Nebraska, Lincoln, NE, 68588, USA

    Jamil Saquer & Jitender S. Deogun

Authors
  1. Jamil Saquer
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  2. Jitender S. Deogun
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Editor information

Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Professor Emeritus, University of Tokyo,  

    Setsuo Ohsuga

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© 1999 Springer-Verlag Berlin Heidelberg

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Saquer, J., Deogun, J.S. (1999). Formal Rough Concept Analysis. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_13

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  • DOI: https://doi.org/10.1007/978-3-540-48061-7_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66645-5

  • Online ISBN: 978-3-540-48061-7

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