DocumentCode :
2310855
Title :
On kernel fuzzy c-means for data with tolerance using explicit mapping for kernel data analysis
Author :
Kanzawa, Yuchi ; Endo, Yasunori ; Miyamoto, Sadaaki
Author_Institution :
Dept. of Commun. Eng., Shibaura Inst. of Technol., Tokyo, Japan
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
6
Abstract :
An explicit mapping is generally unknown for kernel data analysis but their inner product should be known. Though kernel fuzzy c-means algorithm for data with tolerance has been proposed by the authors, the cluster centers and the tolerance in higher dimensional space have been unseen. Contrary to this common assumption, an explicit mapping has been introduced by one of the authors and the situation of kernel fuzzy c-means in higher dimensional space has been described via kernel principal component analysis using the explicit mapping. In this paper, the cluster centers and the tolerance of kernel fuzzy c-means for data with tolerance are described via kernel principal component analysis using the explicit mapping.
Keywords :
data analysis; fuzzy set theory; pattern clustering; principal component analysis; cluster centers; data with tolerance; explicit mapping; kernel data analysis; kernel fuzzy c-means algorithm; kernel principal component analysis; Algorithm design and analysis; Clustering algorithms; Data analysis; Entropy; Kernel; Principal component analysis; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location :
Barcelona
ISSN :
1098-7584
Print_ISBN :
978-1-4244-6919-2
Type :
conf
DOI :
10.1109/FUZZY.2010.5584569
Filename :
5584569
Link To Document :
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