Possibility theoretic clustering

Shitong Wang; Fu Lai Korris Chung; Min Xu; Dewen Hu; Lin Qing

Possibility theoretic clustering

Shitong Wang, Fu Lai Korris Chung, Min Xu, Dewen Hu, Lin Qing

Research output: Journal article publication › Conference article › Academic research › peer-review

Abstract

Based on the exponential possibility model, the possibility theoretic clustering algorithm is proposed in this paper. The new algorithm is distinctive in determining an appropriate number of clusters for a given dataset while obtaining a quality clustering result. The proposed algorithm can be easily implemented using an alternative minimization iterative procedure and its parameters can be effectively initialized by the Parzon window technique and Yager's probability-possibility transformation. Our experimental results demonstrate its success in artificial datasets.

Original language	English
Pages (from-to)	849-858
Number of pages	10
Journal	Lecture Notes in Computer Science
Volume	3644
Issue number	PART I
Publication status	Published - 31 Oct 2005
Event	International Conference on Intelligent Computing, ICIC 2005 - Hefei, China Duration: 23 Aug 2005 → 26 Aug 2005

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Cite this

@article{102947ec0b994371a780fc91cb77a754,

title = "Possibility theoretic clustering",

abstract = "Based on the exponential possibility model, the possibility theoretic clustering algorithm is proposed in this paper. The new algorithm is distinctive in determining an appropriate number of clusters for a given dataset while obtaining a quality clustering result. The proposed algorithm can be easily implemented using an alternative minimization iterative procedure and its parameters can be effectively initialized by the Parzon window technique and Yager's probability-possibility transformation. Our experimental results demonstrate its success in artificial datasets.",

author = "Shitong Wang and Chung, {Fu Lai Korris} and Min Xu and Dewen Hu and Lin Qing",

year = "2005",

month = oct,

day = "31",

language = "English",

volume = "3644",

pages = "849--858",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Verlag",

number = "PART I",

note = "International Conference on Intelligent Computing, ICIC 2005 ; Conference date: 23-08-2005 Through 26-08-2005",

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TY - JOUR

T1 - Possibility theoretic clustering

AU - Wang, Shitong

AU - Chung, Fu Lai Korris

AU - Xu, Min

AU - Hu, Dewen

AU - Qing, Lin

PY - 2005/10/31

Y1 - 2005/10/31

N2 - Based on the exponential possibility model, the possibility theoretic clustering algorithm is proposed in this paper. The new algorithm is distinctive in determining an appropriate number of clusters for a given dataset while obtaining a quality clustering result. The proposed algorithm can be easily implemented using an alternative minimization iterative procedure and its parameters can be effectively initialized by the Parzon window technique and Yager's probability-possibility transformation. Our experimental results demonstrate its success in artificial datasets.

AB - Based on the exponential possibility model, the possibility theoretic clustering algorithm is proposed in this paper. The new algorithm is distinctive in determining an appropriate number of clusters for a given dataset while obtaining a quality clustering result. The proposed algorithm can be easily implemented using an alternative minimization iterative procedure and its parameters can be effectively initialized by the Parzon window technique and Yager's probability-possibility transformation. Our experimental results demonstrate its success in artificial datasets.

UR - http://www.scopus.com/inward/record.url?scp=27144453352&partnerID=8YFLogxK

M3 - Conference article

SN - 0302-9743

VL - 3644

SP - 849

EP - 858

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

IS - PART I

T2 - International Conference on Intelligent Computing, ICIC 2005

Y2 - 23 August 2005 through 26 August 2005

ER -

Possibility theoretic clustering

Abstract

ASJC Scopus subject areas

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