Lindig’s Algorithm for Concept Lattices over Graded Attributes

  • Radim Belohlavek
  • Bernard De Baets
  • Jan Outrata
  • Vilem Vychodil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4617)


Formal concept analysis (FCA) is a method of exploratory data analysis. The data is in the form of a table describing relationship between objects (rows) and attributes (columns), where table entries are grades representing degrees to which objects have attributes. The main output of FCA is a hierarchical structure (so-called concept lattice) of conceptual clusters (so-called formal concepts) present in the data. This paper focuses on algorithmic aspects of FCA of data with graded attributes. Namely, we focus on the problem of generating efficiently all clusters present in the data together with their subconcept-superconcept hierarchy. We present theoretical foundations, the algorithm, analysis of its efficiency, and comparison with other algorithms.


Fuzzy Logic Formal Concept Concept Lattice Formal Concept Analysis Fuzzy 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Radim Belohlavek
    • 1
    • 3
  • Bernard De Baets
    • 2
  • Jan Outrata
    • 3
  • Vilem Vychodil
    • 3
  1. 1.Dept. Systems Science and Industrial Engineering, T. J. Watson School of Engineering and Applied Science, Binghamton University–SUNY, PO Box 6000, Binghamton, NY 13902–6000USA
  2. 2.Dept. Appl. Math., Biometrics, and Process Control, Ghent University, Coupure links 653, B-9000 GentBelgium
  3. 3.Dept. Computer Science, Palacky University, Olomouc, Tomkova 40, CZ-779 00 OlomoucCzech Republic

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