Author(s): Haadvitha Nimmagadda, Sindhu Sukhavasi, S. S. Babu

Email(s): haadvitha.nimmagadda@yahoo.com

DOI: Not Available

Address: Haadvitha Nimmagadda1*, Sindhu Sukhavasi2, S. S. Babu3
1Dept of EEE, RVR and JCCE Engineering College, Guntur, Andhra Pradesh.
2Dept of ECE, Nagarjuna University, Guntur, Andhra Pradesh.
3Bapatla college, Bapatla, Andhra Pradesh.
*Corresponding Author

Published In:   Volume - 3,      Issue - 4,     Year - 2012


ABSTRACT:
The Self-Organizing Map (SOM) is an unsupervised neural network based on competitive learning. It projects high-dimensional input data onto a low dimensional (usually two-dimensional) space. Because it preserves the neighborhood relations of the input data, the SOM is a topology-preserving technique. A topographic map is a two-dimensional, nonlinear approximation of a potentially high-dimensional data manifold, which makes it an appealing instrument for visualizing and exploring high-dimensional data. The Self-Organizing Map (SOM) is the most widely used algorithm, and it has led to thousands of applications in very diverse areas. In this study, we will introduce the SOM algorithm, discuss its properties and applications, and also discuss some of its extensions and new types of topographic map formation, such as the ones that can be used for processing categorical data, time series and tree structured data.


Cite this article:
Haadvitha Nimmagadda, Sindhu Sukhavasi, S. S. Babu. Self Organising Maps: An Interesting Tool for Exploratory Data Analysis. Research J. Engineering and Tech. 3(4): Oct-Dec. 2012 page 319-326.


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