High Dimensional Data Visualization Using Self Organizing Maps
(Sprache: Englisch)
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...
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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.
Bibliographische Angaben
- Autoren: Vikas Chaudhary , R. S. Bhatia , Anil K. Ahlawat
- 2018, 52 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 3659818178
- ISBN-13: 9783659818172
Sprache:
Englisch
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