NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
Initialized RHPNN for fault detection in MEMS
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Analog fault detection using two stage RBF neural network
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
Development of a soldering quality classifier system using a hybrid data mining approach
Expert Systems with Applications: An International Journal
Hybrid neural network model based on multi-layer perceptron and adaptive resonance theory
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A kernel function method in clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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This paper presents a two-stage approach that is effective for performing fast clustering. First, a competitive neural network (CNN) that can harmonize mean squared error and information entropy criteria is employed to exploit the substructure in the input data by identifying the local density centers. A Gravitation neural network (GNN) then takes the locations of these centers as initial weight vectors and undergoes an unsupervised update process to group the centers into clusters. Each node (called gravi-node) in the GNN is associated with a finite attraction radius and would be attracted to a nearby centroid simultaneously during the update process, creating the Gravitation-like behavior without incurring complicated computations. This update process iterates until convergence and the converged centroid corresponds to a cluster. Compared to other clustering methods, the proposed clustering scheme is free of initialization problem and does not need to pre-specify the number of clusters. The two-stage approach is computationally efficient and has great flexibility in implementation. A fully parallel hardware implementation is very possible.