On modeling data mining with granular computing

Author

Yao, Y.Y.

Author_Institution

Dept.of Comput. Sci., Regina Univ., Saskatoon, Sask., Canada

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

638

Lastpage

643

Abstract

This paper deals with the formal and mathematical modeling of data mining. A framework is proposed for rule mining based on granular computing. It is developed in the Tarski´s style through the notions of a model and satisfiability. The model is a database consisting of a finite set of objects described by a finite set of attributes. Within this framework, a concept is defined as a pair consisting of the intension, an expression in a certain language over the set of attributes, and an extension of the concept, a subset of the universe. An object satisfies the expression of a concept if the object has the properties as specified by the expression, and the object belongs to the extension of the concepts. Rules are used to describe relationships between concepts. A rule is expressed in terms of the intentions of the two concepts and is interpreted in terms of the extensions of the concepts. Two interpretations of rules are examined in detail, one is based on the logical implication and the other on the conditional probability

Keywords

computability; data analysis; data mining; database management systems; Tarski style; data analysis; data mining; database; granular computing; Algorithm design and analysis; Computer science; Data mining; Databases; Mathematical model; Testing; Uniform resource locators;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Software and Applications Conference, 2001. COMPSAC 2001. 25th Annual International

Conference_Location

Chicago, IL

ISSN

0730-3157

Print_ISBN

0-7695-1372-7

Type

conf

DOI

10.1109/CMPSAC.2001.960680

Filename

960680