Clustering of volcanic ash arising from different fragmentation mechanisms using Kohonen self-organizing maps

In this study, we present the visualization and clustering capabilities of self-organizing maps (SOM) for analyzing high-dimensional data. We used SOM because they implement an orderly mapping of a high-dimensional distribution onto a regular low-dimensional grid. We used surface texture parameters of volcanic ash that arose from different fragmentation mechanisms as input data. We found that SOM cluster 13-dimensional data more accurately than conventional statistical classifiers. The component planes constructed by SOM are more successful than statistical tests in determining the distinctive parameters.