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ASME Press Select Proceedings

Intelligent Engineering Systems through Artificial Neural Networks Volume 18

Editor
Cihan H. Dagli
Cihan H. Dagli
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ISBN-10:
0791802823
ISBN:
9780791802823
No. of Pages:
700
Publisher:
ASME Press
Publication date:
2008

Clustering is a form of unsupervised learning which can partition data into subsets based upon input attributes and distance metrics such as Euclidean. Clustering is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes or do not efficiently solve the cases with overlapping clusters.

This paper introduces a new strategy to clustering based on shape-adaptive potential functions and optimization procedure for positioning of the cluster centers during the learning process. The two fundamental components of SYNNC are potential function generators (PFGs) using symmetrical kernels and...

Abstract
Introduction
Neural Network Topology
Learning Algorithm
Experiments and Discussions
Conclusions
References
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