Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines | Zendy

Sadaaki Miyamoto | Zendy; Y. Nakayama | Zendy

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Algorithms of Hard c-Means Clustering Using Kernel Functions in Support Vector Machines

Author(s) -

Sadaaki Miyamoto,

Y. Nakayama

Publication year - 2003

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.2003.p0019

Subject(s) - computer science , cluster analysis , support vector machine , kernel method , kernel (algebra) , artificial intelligence , algorithm , pattern recognition (psychology) , data mining , machine learning , mathematics , combinatorics

We discuss hard c-means clustering using a mapping into a high-dimensional space considered within the theory of support vector machines. Two types of iterative algorithms are developed. Effectiveness of the proposed method is shown by numerical examples.

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