High Dimensional Data Visualization Using Self Organizing Maps - R. S. Bhatia,Anil K. Ahlawat,Vikas Chaudhary
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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 d ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | R. S. Bhatia, Anil K. Ahlawat, Vikas Chaudhary |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2018 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783659818172 |