Generalizing Possibility-Based Fuzzy Relational Models | Zendy

Michinori Nakata | Zendy

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Generalizing Possibility-Based Fuzzy Relational Models

Author(s) -

Michinori Nakata

Publication year - 2006

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2006.p0633

Subject(s) - ambiguity , attribute domain , computer science , variable and attribute , fuzzy logic , value (mathematics) , fuzzy set , data mining , relational calculus , mathematics , relational model , relational database , theoretical computer science , artificial intelligence , rough set , machine learning , programming language

The generalized possibility-based fuzzy relational model we propose frees possibility-based fuzzy relational models from the semantic ambiguity and the indistinguishability of membership attribute values. We demonstrate extended relational algebra in this data model. To prevent the semantic ambiguity, a membership attribute is attached to every attribute. This clarifies where each membership attribute value comes from. What each membership attribute value means depends on the property of that attribute. To prevent the indistinguishability of membership attribute values, the value is expressed in a possibility distribution in interval [0,1]. This clarifies what effects the imprecise data value allowed for an attribute has on the membership attribute value. No semantic ambiguity and no indistinguishability of membership attribute values therefore exists in the generalized possibility-based fuzzy relational model.

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