Algorithms for clustering data
Algorithms for clustering data
Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
An introduction to neural computing
An introduction to neural computing
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Building neural networks
Neural network design
Clustering Algorithms
Artificial Neural Networks
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications
Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications
Self-Organizing Maps
Identification of lithofacies using Kohonen self-organizing maps
Computers & Geosciences
Using self-organizing maps to visualize high-dimensional data
Computers & Geosciences
Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico
Expert Systems with Applications: An International Journal
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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.