Visualization of complex datasets with the self-organizing spanning tree

Abstract:

Visualization of real world data is a difficult task due to the high-dimensional and the complex structure in real datasets. Scientific data visualization requires a variety of mathematical techniques to transform high-dimensional data sets into simple graphical objects that provide a clearer understanding. In this work a Self-Organizing Spanning Tree is proposed, which is able to learn a tree topology without any prespecified structure. Experimental results are provided to show the good performance with synthetic and real data. Moreover, the proposed self-organizing model is applied to color vector quantization, whose comparative results are provided.

Año de publicación:

2015

Keywords:

• Visualization
• Spanning trees
• unsupervised learning
• Self-organizing map topologies

Fuente:

scopus