NDLI: Modeling the real world for data mining: granular computing approach

Content Provider	IEEE Xplore Digital Library
Author	Lin, T.Y. Louie, E.
Copyright Year	2001
Description	Author affiliation: Dept. of Math. & Comput. Sci., San Jose State Univ., CA, USA (Lin, T.Y.)
Abstract	In logic, a "real world" is modeled by a Cantor set with relational structure. In this paper, the relational structure is confined to the simplest kind, namely, binary relations. From different consideration, in granular computing, such a binary relational structure has been called a crisp/fuzzy binary granulation, or binary neighborhood system (FBNS). Intuitively, the set has been granulated into binary neighborhoods (generalized equivalence classes). Combining the two views, the simplest kind of "real world" model is BNS-space. From this view, the classical relational theory is the knowledge representation of the universe whose structure is a finite set of equivalence relations; in a "real world" relational theory, a finite set of crisp/fuzzy binary relations. Here knowledge representation is assigning meaningful names to binary neighborhoods (or equivalence classes in relational theory). Depending on the structures, the model can be useful in fuzzy logic or data mining. The focus of this paper is on data mining using granular computing. Experiments show that the computing is extremely fast and the cost of computing extra semantics is very small.
Sponsorship	Int. Fuzzy Syst. Assoc.
Starting Page	3044
Ending Page	3049
File Size	489468
Page Count	6
File Format	PDF
ISBN	0780370783
DOI	10.1109/NAFIPS.2001.943713
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Fuzzy systems Fuzzy sets Knowledge representation Mathematics Computer science Mathematical model Fuzzy set theory Fuzzy logic Costs
Content Type	Text
Resource Type	Article

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