Relational fuzzy c-lines clustering derived from kernelization of fuzzy c-lines

研究成果: Article

2 引用 (Scopus)

抄録

In this paper, two linear fuzzy clustering algorithms are proposed for relational data based on kernel fuzzy c-means, in which the prototypes of clusters are given by lines spanned in a feature space defined by the kernel which is derived from a given relational data. The proposed algorithms contrast the conventional method in which the prototypes of clusters are given by lines spanned by two representative objects. Through numerical examples, it is shown that the proposed algorithms can capture local sub-structures in relational data.

元の言語English
ページ(範囲)175-181
ページ数7
ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
18
発行部数2
出版物ステータスPublished - 2014 3

Fingerprint

Fuzzy clustering
Clustering algorithms

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

これを引用

@article{2d3883d73e3f484db8dfbd1fd7e6a864,
title = "Relational fuzzy c-lines clustering derived from kernelization of fuzzy c-lines",
abstract = "In this paper, two linear fuzzy clustering algorithms are proposed for relational data based on kernel fuzzy c-means, in which the prototypes of clusters are given by lines spanned in a feature space defined by the kernel which is derived from a given relational data. The proposed algorithms contrast the conventional method in which the prototypes of clusters are given by lines spanned by two representative objects. Through numerical examples, it is shown that the proposed algorithms can capture local sub-structures in relational data.",
keywords = "Kernel fuzzy clustering, Relational fuzzy clustering",
author = "Yuchi Kanzawa",
year = "2014",
month = "3",
language = "English",
volume = "18",
pages = "175--181",
journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics",
issn = "1343-0130",
publisher = "Fuji Technology Press",
number = "2",

}

TY - JOUR

T1 - Relational fuzzy c-lines clustering derived from kernelization of fuzzy c-lines

AU - Kanzawa, Yuchi

PY - 2014/3

Y1 - 2014/3

N2 - In this paper, two linear fuzzy clustering algorithms are proposed for relational data based on kernel fuzzy c-means, in which the prototypes of clusters are given by lines spanned in a feature space defined by the kernel which is derived from a given relational data. The proposed algorithms contrast the conventional method in which the prototypes of clusters are given by lines spanned by two representative objects. Through numerical examples, it is shown that the proposed algorithms can capture local sub-structures in relational data.

AB - In this paper, two linear fuzzy clustering algorithms are proposed for relational data based on kernel fuzzy c-means, in which the prototypes of clusters are given by lines spanned in a feature space defined by the kernel which is derived from a given relational data. The proposed algorithms contrast the conventional method in which the prototypes of clusters are given by lines spanned by two representative objects. Through numerical examples, it is shown that the proposed algorithms can capture local sub-structures in relational data.

KW - Kernel fuzzy clustering

KW - Relational fuzzy clustering

UR - http://www.scopus.com/inward/record.url?scp=84897886314&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897886314&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84897886314

VL - 18

SP - 175

EP - 181

JO - Journal of Advanced Computational Intelligence and Intelligent Informatics

JF - Journal of Advanced Computational Intelligence and Intelligent Informatics

SN - 1343-0130

IS - 2

ER -