High Dimensional Data Visualization Using Self Organizing Maps

High Dimensional Data Visualization Using Self Organizing Maps

LAP Lambert Academic Publishing ( 2018-05-11 )

€ 35,90

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A Self-organizing map is a non-linear, unsupervised neural network that is used for data clustering and visualization of high-dimensional data. A Self-organizing map uses U-matrix to visualize the high-dimensional data and the distances between neurons on the map. However, the structure of clusters and their shapes are often distorted. For better visualization of high-dimensional data, a new approach high dimensional data visualization Self-organizing map (HVSOM) is explained. The HVSOM preserve the inter-neuron distance and better visualizes the differences between the clusters. In HVSOM, the distances between input data points on the map resemble same those in the original space.

Book Details:

ISBN-13:

978-3-659-81817-2

ISBN-10:

3659818178

EAN:

9783659818172

Book language:

English

By (author) :

Dr. Vikas Chaudhary
Dr. R.S. Bhatia
Dr. Anil K. Ahlawat

Number of pages:

52

Published on:

2018-05-11

Category:

Other