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High Dimensional Data Visualization Using Self Organizing Maps

High Dimensional Data Visualization Using Self Organizing Mapsvon Vikas Chaudhary Sie sparen 14% des UVP sparen 14%
Über High Dimensional Data Visualization Using Self Organizing Maps

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.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9783659818172
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 52
  • Veröffentlicht:
  • 11. Mai 2018
  • Abmessungen:
  • 150x4x220 mm.
  • Gewicht:
  • 96 g.
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Beschreibung von High Dimensional Data Visualization Using Self Organizing Maps

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.

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