Linear fuzzy clustering using eigenvalues for optimization of dimensional coefficients

Author

Umayahara, Kazutaka ; Miyamoto, Sadaaki ; Nakamori, Yoshiteru

Author_Institution

Japan Adv. Inst. of Sci. & Technol., Ishikawa, Japan

fYear

2001

fDate

25-28 July 2001

Firstpage

2517

Abstract

This paper considers the problem of detecting local linear substructures of a system in a high-dimensional data space by applying a fuzzy clustering technique. We propose a linear fuzzy clustering method using eigenvalues of the fuzzy scatter matrix in the objective function for optimizing the dimensional coefficients. The optimal solutions for the objective function and some illustrative examples are shown in this paper

Keywords

S-matrix theory; dimensions; eigenvalues and eigenfunctions; fuzzy set theory; linear systems; optimisation; pattern clustering; dimensional coefficients optimization; eigenvalues; fuzzy scatter matrix; high-dimensional data space; linear fuzzy clustering; local linear substructure detection; objective function optimal solutions; Clustering algorithms; Clustering methods; Data engineering; Ear; Eigenvalues and eigenfunctions; Fuzzy sets; Fuzzy systems; Scattering; Space technology; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.943618

Filename

943618

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1750563