Alternative c-means clustering algorithms

In this paper we propose a new metric to replace the Euclidean norm in c-means clustering procedures. On the basis of the robust statistic and the influence function, we claim that the proposed new metric is more robust than the Euclidean norm. We then create two new clustering methods called the al...

Full description

Saved in:
Bibliographic Details
Published inPattern recognition Vol. 35; no. 10; pp. 2267 - 2278
Main Authors Wu, Kuo-Lung, Yang, Miin-Shen
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.10.2002
Subjects
Alternative c-means
Fixed-point iterations
Fuzzy c-means
Hard c-means
Noise
Robustness
Alternative c-means
Hard c-means
Robustness
Noise
Fixed-point iterations
Fuzzy c-means
Online AccessGet full text
ISSN0031-3203
1873-5142
DOI10.1016/S0031-3203(01)00197-2

Cover

More Information
Summary:In this paper we propose a new metric to replace the Euclidean norm in c-means clustering procedures. On the basis of the robust statistic and the influence function, we claim that the proposed new metric is more robust than the Euclidean norm. We then create two new clustering methods called the alternative hard c-means (AHCM) and alternative fuzzy c-means (AFCM) clustering algorithms. These alternative types of c-means clustering have more robustness than c-means clustering. Numerical results show that AHCM has better performance than HCM and AFCM is better than FCM. We recommend AFCM for use in cluster analysis. Recently, this AFCM algorithm has successfully been used in segmenting the magnetic resonance image of Ophthalmology to differentiate the abnormal tissues from the normal tissues.
ISSN:0031-3203
1873-5142
DOI:10.1016/S0031-3203(01)00197-2