Thursday 28th of March 2024
 

A study on the behavior of a neural network for grouping the data


Suneetha Chittineni and 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.

Keywords: Data Preprocessing, Normalization, Fast Learning Artificial Neural network

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ABOUT THE AUTHORS

Suneetha Chittineni
Suneetha Chittineni, Associate Professor in the department of Computer Applications, R.V.R.& J.C College of Engineering,Chowdavarm,Guntur. She has 12 years of teaching experience. Currently She is persuing Ph.D from Acharya Nagarjuna University, Guntur. Her research interests include Artificial Intelligence, Machine learning, Pattern Recognition.

Raveendra Babu Bhogapathi
Dr B. Raveendra Babu obtained his Masters in Computer Science and Engineering from Anna University, Chennai. He received his Ph.D. in Applied Mathematics at S.V University, Tirupati. He is currently leading a Team as Director (Operations), M/s. Delta Technologies (P) Ltd., Madhapur, Hyderabad. He has 26 years of teaching experience. He has more than 25 International & National publications to his credit. His research areas of interest include VLDB, Image Processing, Pattern Analysis and Information Security


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