Citation

Chang, Roy Kwang Yang and Loo, Chu Kiong, Chu Kiong and Rao, , Machavaram V. C. (2006) Autonomous and deterministic probabilistic neural network using global k-means. ADVANCES IN NEURAL NETWORKS - ISNN 2006, 3971 (1). pp. 830-836. ISSN 0302-9743

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Abstract

We present a comparative study between Expectation-Maximization (EM) trained probabilistic neural network (PNN) with random initialization and with initialization from Global k-means. To make the results more comprehensive, the algorithm was tested on both homoscedastic and heteroscedastic PNNs. Normally, user have to define the number of clusters through trial and error method, which makes random initialization to be of stochastic nature. Global k-means was chosen as the initialization method because it can autonomously find the number of clusters using a selection criterion and can provide deterministic clustering results. The proposed algorithm was tested on benchmark datasets and real world data from the cooling water system in a power plant.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	10 Aug 2011 07:04
Last Modified:	10 Aug 2011 07:04
URII:	http://shdl.mmu.edu.my/id/eprint/2066

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