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
U2 - 10.20965/jaciii.2014.p0175
DO - 10.20965/jaciii.2014.p0175
M3 - Article
AN - SCOPUS:84897886314
SN - 1343-0130
VL - 18
SP - 175
EP - 181
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
IS - 2
ER -