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 re…
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Produktdetails
Weitere Autoren: Bhatia, R. S. / Ahlawat, Anil K.
- ISBN: 978-3-659-81817-2
- EAN: 9783659818172
- Produktnummer: 37452021
- Verlag: LAP Lambert Academic Publishing
- Sprache: Englisch
- Erscheinungsjahr: 2018
- Seitenangabe: 52 S.
- Masse: H22.0 cm x B15.0 cm x D0.3 cm 96 g
- Abbildungen: Paperback
- Gewicht: 96
Über den Autor
Dr. Vikas Chaudhary is working as a Professor in Computer Engineering Department, Madda Walabu University, Bale Robe, Ethiopia. Dr. R.S. Bhatia is working as a Professor in Electrical Engineering Department, National Institute of Technology(NIT), Kurukshetra, India. Dr. Anil K. Ahlawat is working as a Professor in Computer Science & Engineering Department, Krishna Institute of Engineering & Technology(KIET), Ghaziabad, India.
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