A method of identifying influential data in fuzzy clustering

Author

Imai, Hideyuki ; Tanaka, Akira ; Miyakoshi, Masaaki

Author_Institution

Div. of Syst. & Inf. Eng., Hokkaido Univ., Sapporo, Japan

Volume

6

Issue

1

fYear

1998

fDate

2/1/1998 12:00:00 AM

Firstpage

90

Lastpage

101

Abstract

In multivariate statistical methods, it is important to identify influential observations for a reasonable interpretation of the data structure. In this paper, we propose a method for identifying influential data in the fuzzy C-means (FCM) algorithm. To investigate such data, we consider a perturbation of the data points and evaluate the effect of a perturbation. As a perturbation, we consider two cases: one is the case in which the direction of a perturbation is specified and the other is the case in which the direction of a perturbation is not specified. By computing the change in the clustering result of FCM when given data points are slightly perturbed, we can look for data points that greatly affect the result. Also, we confirm an efficacy of the proposed method by numerical examples

Keywords

fuzzy set theory; minimisation; pattern recognition; statistical analysis; fuzzy C-means algorithm; fuzzy clustering; multivariate statistical methods; Clustering algorithms; Clustering methods; Data analysis; Data structures; Fuzzy set theory; Helium; Image segmentation; Partitioning algorithms; Sensitivity analysis; Statistical analysis;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.660810

Filename

660810