Suneetha Chittineni

suneetha chittineni, Raveendra Babu Bhogapathi

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.

Suneetha Chittineni, Raveendra Babu Bhogapathi

One of the frequently stated advantages of neural networks is that they can work effectively with non-normally distributed data. But optimal results are possible with normalized data.In this paper, how normality of the input affects the behaviour of a K-means fast learning artificial neural network(KFLANN) for grouping the data is presented. Basically, the grouping of high dimensional input data is controlled by additional neural network input parameters namely vigilance and tolerance.Neural networks learn faster and give better performance if the input variables are pre-processed before being fed to the input units of the neural network. A common way of dealing with data that is not normally distributed is to perform some form of mathematical transformation on the data that shifts it towards a normal distribution.In a neural network, data preprocessing transforms the data into a format that will be more easily and effectively processed for the purpose of the user. Among various methods, Normalization is one which organizes data for more efficient access. Experimental results on several artificial and synthetic data sets indicate that the groups formed in the data vary with non-normally distributed data and normalized data and also depends on the normalization method used.