Growing Self-Organizing Maps for Data Analysis

Abstract

Currently, there exist many research areas that produce large multivariable datasets that are difficult to visualize in order to extract useful information. Kohonen selforganizing maps have been used successfully in the visualization and analysis of multidimensional data. In this work, a projection technique that compresses multidimensional datasets into two dimensional space using growing self-organizing maps is described. With this embedding scheme, traditional Kohonen visualization methods have been implemented using growing cell structures networks. New graphical map displays have been compared with Kohonen graphs using two groups of simulated data and one group of real multidimensional data selected from a satellite scene.

A Generic Framework for Behavior Recognition of Complex Activities in Robotics

“…9n contrast, a large SOM with thousands of units (sometimes called emergent map (ESOM) [23]) enables a better clustering with the risk of over-fitting if not enough training data is available. To avoid these problems, approaches of dynamically growing maps were developed [24], [25].…”