Direct Factorization by Similarity of Fuzzy Concept Lattices by Factorization of Input Data

2008 ◽  
pp. 68-79 ◽  
Author(s):  
Radim Belohlavek ◽  
Jan Outrata ◽  
Vilem Vychodil
Keyword(s):  
Input Data ◽  
Concept Lattices ◽  
Fuzzy Concept ◽  
Direct Factorization

scholarly journals Gradation of Fuzzy Preconcept Lattices

Axioms ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 41
Author(s):  
Alexander Šostak ◽  
Ingrīda Uļjane ◽  
Māris Krastiņš
Keyword(s):  
Fuzzy Information ◽  
Concept Lattices ◽  
Practical Applications ◽  
Fuzzy Concept ◽  
The One ◽  
Practical Nature

Noticing certain limitations of concept lattices in the fuzzy context, especially in view of their practical applications, in this paper, we propose a more general approach based on what we call graded fuzzy preconcept lattices. We believe that this approach is more adequate for dealing with fuzzy information then the one based on fuzzy concept lattices. We consider two possible gradation methods of fuzzy preconcept lattice—an inner one, called D-gradation and an outer one, called M-gradation, study their properties, and illustrate by a series of examples, in particular, of practical nature.

Knowledge Reduction in Crisply Generated Fuzzy Concept Lattices

2015 ◽  
Vol 142 (1-4) ◽  
pp. 307-335 ◽  
Author(s):  
Mei-Zheng Li ◽  
Guo-Yin Wang
Keyword(s):  
Concept Lattices ◽  
Knowledge Reduction ◽  
Fuzzy Concept

On some general aspects of forming fuzzy concept lattices

2013 ◽  
Vol 7 ◽  
pp. 5599-5605 ◽  
Author(s):  
Jozef Pocs ◽  
Jana Pocsova
Keyword(s):  
Concept Lattices ◽  
Fuzzy Concept ◽  
General Aspects

The Construction of Fuzzy Concept Lattices Based on (θ, σ)-Fuzzy Rough Approximation Operators

2011 ◽  
Vol 111 (1) ◽  
pp. 33-45 ◽  
Author(s):  
Yanqing Yao ◽  
Jusheng Mi ◽  
Zhoujun Li ◽  
Bin Xie
Keyword(s):  
Concept Lattices ◽  
Fuzzy Concept ◽  
Approximation Operators ◽  
Rough Approximation

CHARACTERIZING TREES IN CONCEPT LATTICES

2008 ◽  
Vol 16 (supp01) ◽  
pp. 1-15 ◽  
Author(s):  
RADIM BĚLOHLÁVEK ◽  
BERNARD DE BAETS ◽  
JAN OUTRATA ◽  
VILEM VYCHODIL
Keyword(s):  
Formal Concept Analysis ◽  
Input Data ◽  
Concept Lattice ◽  
Sufficient Conditions ◽  
Formal Concept ◽  
Clustering Methods ◽  
Concept Lattices ◽  
Classification Schemes ◽  
Formal Concepts ◽  
Necessary And Sufficient

Concept lattices are systems of conceptual clusters, called formal concepts, which are partially ordered by the subconcept/superconcept relationship. Concept lattices are basic structures used in formal concept analysis. In general, a concept lattice may contain overlapping clusters and need not be a tree. On the other hand, tree-like classification schemes are appealing and are produced by several clustering methods. In this paper, we present necessary and sufficient conditions on input data for the output concept lattice to form a tree after one removes its least element. We present these conditions for input data with yes/no attributes as well as for input data with fuzzy attributes. In addition, we show how Lindig's algorithm for computing concept lattices gets simplified when applied to input data for which the associated concept lattice is a tree after removing the least element. The paper also contains illustrative examples.

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