DocumentCode :
2017155
Title :
Non-metric neural clustering
Author :
Sato-Ilic, Mika
Author_Institution :
Inst. of Policy & Planning Sci., Tsukuba Univ., Ibaraki, Japan
Volume :
1
fYear :
1999
fDate :
1999
Firstpage :
72
Abstract :
This paper presents an implementation of a non-metric clustering model using neural networks. The metric clustering model is correctly implemented by using the structure of neural networks based on the universal approximation theorem. However, it might be believed that the estimates as outputs contain little reliable information beyond their rank order. So, I discuss one implementation of a clustering model using a non-metric algorithm of neural networks
Keywords :
neural nets; pattern clustering; neural networks; nonmetric algorithm; nonmetric clustering model; nonmetric neural clustering; universal approximation theorem; Boundary conditions; Clustering algorithms; Fuzzy neural networks; Fuzzy sets; Humans; Neural networks; Reliability theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-5871-6
Type :
conf
DOI :
10.1109/ICONIP.1999.843964
Filename :
843964
Link To Document :
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