Title
On the size of ∃-generalized concept lattices
Abstract
Formal Concept Analysis (FCA) offers several tools for qualitative data analysis. One possibility is to group objects that share common attributes together and get a concept lattice that describes the data. Quite often the size of this concept lattice is very large. Many authors have investigated methods to reduce the size of this lattice. In Kwuida et al. (2014) the authors consider putting together some attributes to reduce the size of the attribute sets. But this reduction does not always carry over to the set of concepts. They provided some counter examples where the size of the concept lattice increases by one after putting two attributes together, and asked the following question: “How many new concepts can be generated by an ∃-generalization on just two attributes?” The present paper provides a family of contexts for which the size increases on more than one concept after putting solely two attributes together.
Year
DOI
Venue
2020
10.1016/j.dam.2019.02.035
Discrete Applied Mathematics
Keywords
DocType
Volume
Formal concept analysis,Concept lattices,Generalizing attributes
Journal
273
ISSN
Citations 
PageRank 
0166-218X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Léonard Kwuida15516.25
Rostand S. Kuitché200.34
Romuald E.A. Temgoua300.34