A Study on the Behavior of a Neural Network for Grouping the Data

TL;DR

This paper investigates how the normality of input data influences the behavior and performance of a K-means fast learning artificial neural network (KFLANN) in data grouping tasks, emphasizing the importance of data preprocessing.

Contribution

It demonstrates the impact of data normalization on neural network grouping performance and analyzes how different normalization methods affect the results.

Findings

Normalized data improves neural network learning speed and accuracy.

Grouping results vary significantly with different normalization methods.

Non-normal data leads to different group formations compared to normalized data.

Abstract

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.