Extracting Concepts From Fuzzy Relational Context Families

Fuzzy relational formal concept analysis (FRCA) mines collections of fuzzy lattices from xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy context families , which are special datasets made contexts and relations between objects different types. Mainly, FRCA consists the following procedures: first, an initial family is transformed into a collection contexts; second, lattice generated each by using one techniques existing in literature. The principal tools to transform set so-called scaling quantifiers particular quantifiers based on xmlns:xlink="http://www.w3.org/1999/xlink">evaluative linguistic expression . can be applied whenever information needs extracted multirelational including vagueness, it viewed as extension both xmlns:xlink="http://www.w3.org/1999/xlink">relational analysis This article contributes development achieving goals. First all, we present study new class quantifiers, called xmlns:xlink="http://www.w3.org/1999/xlink">t-scaling extract concepts families. Subsequently, provide algorithm generate, given t-scaling quantifier, family, composed pair relation their objects. After that, introduce ordered all allows us discover correspondence among deriving quantifiers. Finally, discuss how results obtained for extended quantifies. Therefore, this highlights main differences