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.
|Journal of Advanced Computational Intelligence and Intelligent Informatics
|Published - 2014 3月
ASJC Scopus subject areas
- コンピュータ ビジョンおよびパターン認識