Open AccessImproving self-organizing map with nguyen-widrow initialization lgorithm
Author(s) -
Maureen Nettie N Linan,
Bobby D. Gerardo,
Ruji P. Medina
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v15.i1.pp535-542
Subject(s) - initialization , computer science , cluster analysis , artificial neural network , self organizing map , artificial intelligence , convergence (economics) , span (engineering) , cluster (spacecraft) , pattern recognition (psychology) , algorithm , engineering , civil engineering , economics , programming language , economic growth
The quality of cluster result and the learning speed of Self-organizing map (SOM) are dependent on the initialization of weights since the initial values for weight vectors affect the performance of SOM training when applied to clustering. In this paper, improvement of SOM was achieved with the application of the Nguyen-Widrow algorithm to initialize weights. Nguyen-Widrow initialization algorithm is a method for initialization of the weights of neural networks to speed up the training process. Performance of the modified SOM was determined in terms of cluster error rate and the number of iterations to achieve convergence using different datasets and results show that the modified SOM algorithm produces better cluster results and improved training speed compared to traditional SOM.