Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)

The New Method Definite Initial Cluster Center for Fuzzy Risk Clustering Neural Networks

Authors
Kaiqi Zou1, Jie Cui
1College of Information Engineering, Dalian University
Corresponding Author
Kaiqi Zou
Available Online October 2007.
DOI
10.2991/iske.2007.64How to use a DOI?
Keywords
density; grid; initial cluster center; fuzzy risk clustering neural networks
Abstract

Neural network has a powerful parallel processing capability, along with its rise in the fuzzy risk cluster analysis which has occupied an important position, however, the quality of fuzzy risk clustering results is influenced by the initial value of options. The initial cluster center method for fuzzy risk clustering neural network, based on density and grid method, automatically determine the number of clusters and the initial cluster centers. Compared with the classical method simulation FCM, we can see a valid and effective method that can effectively speed up the convergence.

Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
Series
Advances in Intelligent Systems Research
Publication Date
October 2007
ISBN
978-90-78677-04-8
ISSN
1951-6851
DOI
10.2991/iske.2007.64How to use a DOI?
Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Kaiqi Zou
AU  - Jie Cui
PY  - 2007/10
DA  - 2007/10
TI  - The New Method Definite Initial Cluster Center for Fuzzy Risk Clustering Neural Networks
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 381
EP  - 384
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.64
DO  - 10.2991/iske.2007.64
ID  - Zou2007/10
ER  -