  K-means Fast Learning Artificial Neural Network (K-FLANN) is an unsupervised
neural network requires two parameters: tolerance and vigilance. Best
Clustering results are 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.
