Characterization and detection of noise in clustering

Document Type

Article

Publication Date

1-1-1991

Abstract

A concept of 'Noise Cluster' is introduced such that noisy data points may be assigned to the noise class. The approach is developed for objective functional type (K-means or fuzzy K-means) algorithms, and its ability to detect 'good' clusters amongst noisy data is demonstrated. The approach presented is applicable to a variety of fuzzy clustering algorithms as well as regression analysis. © 1991.

Identifier

0000586827 (Scopus)

Publication Title

Pattern Recognition Letters

External Full Text Location

https://doi.org/10.1016/0167-8655(91)90002-4

ISSN

01678655

First Page

657

Last Page

664

Issue

11

Volume

12

This document is currently not available here.

Share

COinS