Self-organizing maps
Cluster validity methods: part I
ACM SIGMOD Record
Distance Matrix Based Clustering of the Self-Organizing Map
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Hierarchical fuzzy filter method for unsupervised feature selection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Unsupervised fuzzy-rough set-based dimensionality reduction
Information Sciences: an International Journal
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This study presents an unsupervised feature selection and learning approach for the discovery and intuitive imaging of significant temporal patterns in seismic single-station or network recordings. For this purpose, the data are parametrized by real-valued feature vectors for short time windows using standard analysis tools for seismic data, such as frequency-wavenumber, polarization, and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure, which is in particular suitable for high-dimensional data sets. Our feature selection method is based on significance testing using the Wald-Wolfowitz runs test for individual features and on correlation hunting with SOMs in feature subsets. Using synthetics composed of Rayleigh and Love waves and real-world data, we show the robustness and the improved discriminative power of that approach compared to feature subsets manually selected from individual wavefield parametrization methods. Furthermore, the capability of the clustering and visualization techniques to investigate the discrimination of wave phases is shown by means of synthetic waveforms and regional earthquake recordings.