Literature DB >> 15369100

Analysis of the weighting exponent in the FCM.

Jian Yu1, Qiansheng Cheng, Houkuan Huang.   

Abstract

The fuzzy c-means (FCM) algorithm is one of the most frequently used clustering algorithms. The weighting exponent m is a parameter that greatly influences the performance of the FCM. But there has been no theoretical basis for selecting the proper weighting exponent in the literature. In this paper, we develop a new theoretical approach to selecting the weighting exponent in the FCM. Based on this approach, we reveal the relation between the stability of the fixed points of the FCM and the data set itself. This relation provides the theoretical basis for selecting the weighting exponent in the FCM. The numerical experiments verify the effectiveness of our theoretical conclusion.

Entities:  

Year:  2004        PMID: 15369100     DOI: 10.1109/tsmcb.2003.810951

Source DB:  PubMed          Journal:  IEEE Trans Syst Man Cybern B Cybern        ISSN: 1083-4419


  3 in total

1.  Consistent segmentation using a Rician classifier.

Authors:  Snehashis Roy; Aaron Carass; Pierre-Louis Bazin; Susan Resnick; Jerry L Prince
Journal:  Med Image Anal       Date:  2011-12-13       Impact factor: 8.545

2.  Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm.

Authors:  Jianfa Wu; Dahao Peng; Zhuping Li; Li Zhao; Huanzhang Ling
Journal:  PLoS One       Date:  2015-03-25       Impact factor: 3.240

3.  Clustering of fMRI data: the elusive optimal number of clusters.

Authors:  Mohamed L Seghier
Journal:  PeerJ       Date:  2018-10-03       Impact factor: 2.984

  3 in total

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