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K-means fast learning artificial neural network (K-FLANN) algorithm begins with the initialization of two parameters vigilance and tolerance which are the key to get optimal clustering outcome. The optimization task is to change these parameters so a desired mapping between inputs and outputs (clusters) of the KFLANN is achieved. This study presents finding the behavioral parameters of K-FLANN that yield good clustering performance using an optimization method known as Differential Evolution. DE algorithm is a simple efficient meta-heuristic for global optimization over continuous spaces. The K-FLANN algorithm is modified to select winning neuron (centroid) for a data member in order to improve the matching rate from input to output. The experiments were performed to evaluate the proposed work using machine learning artificial data sets for classification problems and synthetic data sets. The simulation results have revealed that optimization of K-FLANN has given quite promising results in terms of convergence rate and accuracy when compared with other algorithms. Also the comparisons are made between K-FLANN and modified KFLANN.

Finding Initial Parameters of Neural Network for Data Clustering