High Dimensional Data Visualization Using Self Organizing Maps

Chaudhary, Dr. Vikas

ISBN 10: 3659818178 / ISBN 13: 9783659818172

Editore: LAP LAMBERT Academic Publishing, 2018

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Codice articolo wbs9546115827

Dati bibliografici

• Titolo: High Dimensional Data Visualization Using Self Organizing Maps
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 3659818178
• ISBN 13: 9783659818172
• Rilegatura: Brossura
• Condizione: Very Good
• Peso: 127 grammi

Descrizione articolo

Riassunto

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.