High Dimensional Data Visualization Using Self Organizing Maps - Chaudhary, Vikas;Bhatia, R. S.;Ahlawat, Anil K.
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
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.