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