期刊名称：International Journal of Computer Science, Engineering and Applications (IJCSEA)
印刷版ISSN：2231-0088
电子版ISSN：2230-9616
出版年度：2013
卷号：3
期号：5
DOI：10.5121/ijcsea.2013.350213
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：K-means Fast Learning Artificial Neural Network (K-FLANN) is an unsupervised neural network requires two parameters: tolerance and vigilance. Best Clustering results a re feasible only by finest parameters specified to the neural network. Selecting optimal values for these parameters is a major problem . To solve this issue, Genetic Algorithm (GA) is used to determine optimal parameters of K-FLANN for finding groups in multidimensional data. K-FLANN is a simple topological network, in which output nodes grows dynamically during the clustering process on receiving input patterns. Original K-FLANN is enhanced to select winner unit out of the matched nodes so that stable clusters are formed with in a less number of epochs. The experimental results show that the GA is efficient in finding optimal values of parameters from the large search space and is tested using artificial and synthetic data sets
关键词：Clustering; Fast learning artificial neural network; Genetic algorithm; Fitness function
