Title :
Non-metric neural clustering
Author_Institution :
Inst. of Policy & Planning Sci., Tsukuba Univ., Ibaraki, Japan
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;
Conference_Titel :
Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-5871-6
DOI :
10.1109/ICONIP.1999.843964