Relational Fuzzy c-Lines Clustering Derived from Kernelization of Fuzzy c-Lines | Zendy

Yuchi Kanzawa | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Relational Fuzzy c-Lines Clustering Derived from Kernelization of Fuzzy c-Lines

Author(s) -

Yuchi Kanzawa

Publication year - 2014

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2014.p0175

Subject(s) - kernelization , fuzzy clustering , computer science , relational database , cluster analysis , fuzzy logic , kernel (algebra) , fuzzy classification , data mining , fuzzy set , artificial intelligence , algorithm , pattern recognition (psychology) , theoretical computer science , mathematics , discrete mathematics , parameterized complexity

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research