Improvement and optimization of a fuzzy C-means clustering algorithm

Y. Shen; H. Shi; J. Q. Zhang

Improvement and optimization of a fuzzy C-means clustering algorithm

Y. Shen^*
, H. Shi
, J. Q. Zhang

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology

Research output: Contribution to conference › Paper › peer-review

18 Scopus citations

Abstract

In this paper, an improved FCM clustering algorithm is proposed. Unlike a traditional FCM clustering algorithm whose convergence is sensitive to its initial parameters, the proposed algorithm based on fuzzy decision theory can automatically and adaptively select these parameters with optimal values. The simulation results indicate that the modified algorithm not only overcomes the ill phenomena of the FCM algorithms available now, but also is robust to the selection of the weighting constants.

Original language	English
Pages	1430-1433
Number of pages	4
State	Published - 2001
Event	18th IEEE Instrumentation and Measurement Technology Conference - Budapest, Hungary Duration: 21 May 2001 → 23 May 2001

Conference

Conference	18th IEEE Instrumentation and Measurement Technology Conference
Country/Territory	Hungary
City	Budapest
Period	21/05/01 → 23/05/01

Keywords

Fuzzy C-means
Fuzzy clustering analysis
Fuzzy decision theory

Cite this

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title = "Improvement and optimization of a fuzzy C-means clustering algorithm",

abstract = "In this paper, an improved FCM clustering algorithm is proposed. Unlike a traditional FCM clustering algorithm whose convergence is sensitive to its initial parameters, the proposed algorithm based on fuzzy decision theory can automatically and adaptively select these parameters with optimal values. The simulation results indicate that the modified algorithm not only overcomes the ill phenomena of the FCM algorithms available now, but also is robust to the selection of the weighting constants.",

keywords = "Fuzzy C-means, Fuzzy clustering analysis, Fuzzy decision theory",

author = "Y. Shen and H. Shi and Zhang, \{J. Q.\}",

year = "2001",

language = "英语",

pages = "1430--1433",

note = "18th IEEE Instrumentation and Measurement Technology Conference ; Conference date: 21-05-2001 Through 23-05-2001",

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TY - CONF

T1 - Improvement and optimization of a fuzzy C-means clustering algorithm

AU - Shen, Y.

AU - Shi, H.

AU - Zhang, J. Q.

PY - 2001

Y1 - 2001

N2 - In this paper, an improved FCM clustering algorithm is proposed. Unlike a traditional FCM clustering algorithm whose convergence is sensitive to its initial parameters, the proposed algorithm based on fuzzy decision theory can automatically and adaptively select these parameters with optimal values. The simulation results indicate that the modified algorithm not only overcomes the ill phenomena of the FCM algorithms available now, but also is robust to the selection of the weighting constants.

AB - In this paper, an improved FCM clustering algorithm is proposed. Unlike a traditional FCM clustering algorithm whose convergence is sensitive to its initial parameters, the proposed algorithm based on fuzzy decision theory can automatically and adaptively select these parameters with optimal values. The simulation results indicate that the modified algorithm not only overcomes the ill phenomena of the FCM algorithms available now, but also is robust to the selection of the weighting constants.

KW - Fuzzy C-means

KW - Fuzzy clustering analysis

KW - Fuzzy decision theory

UR - https://www.scopus.com/pages/publications/0034822378

M3 - 论文

AN - SCOPUS:0034822378

SP - 1430

EP - 1433

T2 - 18th IEEE Instrumentation and Measurement Technology Conference

Y2 - 21 May 2001 through 23 May 2001

ER -

Improvement and optimization of a fuzzy C-means clustering algorithm

Abstract

Conference

Keywords

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