High Dimensional Data Visualization Using Self Organizing Maps

ISBN-13: 9783659818172 / Angielski / Miękka / 2018 / 52 str.

Dr. Vikas Chaudhary; Dr. R.S. Bhatia; Dr. Anil K. Ahlawat

Opis

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.