Generalized fuzzy c-shells clustering and detection of circular and elliptical boundaries

Document Type

Article

Publication Date

1-1-1992

Abstract

The Fuzzy c-Shells (FCS) algorithm and its adaptive generalization, called the Adaptive Fuzzy c-Shells (AFCS) algorithm, are considered for detection of curved boundaries, specifically circular and elliptical. The FCS algorithms utilize hyper-spherical-shells as cluster prototypes. Thus in two dimensions, the prototypes are circles. The AFCS algorithms consider hyper-ellipsoidal-shells as prototypes, hence the ability to characterize elliptical boundaries. The generalization is achieved by allowing the distances to be measured through a norm inducing matrix that is symmetric, positive definite. Each cluster is allowed to have a different matrix, which is made a variable of optimization. The ability of the algorithms to detect circular and elliptical boundaries in two-dimensional data is illustrated through several examples. © 1992.

Identifier

0026897712 (Scopus)

Publication Title

Pattern Recognition

External Full Text Location

https://doi.org/10.1016/0031-3203(92)90134-5

ISSN

00313203

First Page

713

Last Page

721

Issue

7

Volume

25

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