A Global k-means Approach for Autonomous Cluster Initialization of Probabilistic Neural Network

This paper focuses on the statistical based Probabilistic Neural Network (PNN) for pattern classification problems with Expectation - Maximization (EM) chosen as the training algorithm. This brings about the problem of random initialization, which means, the user has to predefine the number of clusters through trial and error. Global k-means is used to solve this and to provide a deterministic number of clusters using a selection criterion. On top of that, Fast Global k-means was tested as a substitute for Global k-means, to reduce the computational time taken. Tests were done on both homescedastic and heteroscedastic PNNs using benchmark medical datasets and also vibration data obtained from a U.S. Navy CH-46E helicopter aft gearbox (Westland).