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

The paper presents additional results on factorization by similarity of fuzzy concept lattices. A fuzzy concept lattice is a hierarchically ordered collection of clusters extracted from tabular data. The basic idea of factorization by similarity is to have, instead of a possibly large original fuzzy concept lattice, its factor lattice. The factor lattice contains less clusters than the original concept lattice but, at the same time, represents a reasonable approximation of the original concept lattice and provides us with a granular view on the original concept lattice. The factor lattice results by factorization of the original fuzzy concept lattice by a similarity relation. The similarity relation is specified by a user by means of a single parameter, called a similarity threshold. Smaller similarity thresholds lead to smaller factor lattices, i.e. to more comprehensible but less accurate approximations of the original concept lattice. Therefore, factorization by similarity provides ...

• Type: Conference
• Year: 2006
• Where: CLA
• Authors: Radim Belohlávek, Jan Outrata, Vilém Vychodil
• Keywords: CLA 2006, CLA 2007, Concept Lattice, Fuzzy Concept Lattice, Original Concept Lattice