Hierarchical classification inference for fuzzy data analysis

Author

Van der Lubbe, Jan C A ; Backer, Eric

Author_Institution

Dept. of Electr. Eng., Delft Univ. of Technol., Netherlands

fYear

1995

Firstpage

402

Lastpage

407

Abstract

One of the main problems in fuzzy data analysis is the clustering of data. In this paper an expert system approach is followed. On the basis of training data sets a hierarchical knowledge tree is generated consisting of rules that are characterized by an increasing specificity. The hierarchical knowledge is used for inferring decisions on new data sets to be assessed. In order to reduce further the computational complexity the core zone index is introduced, which guarantees the optimal search level in the hierarchical knowledge tree

Keywords

computational complexity; data analysis; decision theory; expert systems; fuzzy set theory; hierarchical systems; inference mechanisms; knowledge acquisition; learning (artificial intelligence); pattern classification; tree data structures; tree searching; computational complexity; core zone index; data clustering; data set training; decision inference; expert system; fuzzy data analysis; hierarchical classification inference; hierarchical knowledge tree; new data sets; optimal search level; specificity; Character generation; Computational complexity; Data analysis; Expert systems; Information theory; Interference; Statistical analysis; Statistics; Thumb; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., Third International Symposium on

Conference_Location

College Park, MD

Print_ISBN

0-8186-7126-2

Type

conf

DOI

10.1109/ISUMA.1995.527729

Filename

527729