Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering
Pattern Recognition Letters
Unsupervised pattern classification by neural networks
Mathematics and Computers in Simulation - Special issue: signal processing and neural networks
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Self-Organizing Maps
Clustering of EEG-Segments Using Hierarchical Agglomerative Methods and Self-Organizing Maps
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Combining schema and instance information for integrating heterogeneous data sources
Data & Knowledge Engineering
International Journal of Remote Sensing
Clustering
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
Classification of pharmaceutical solid excipients using self-organizing maps
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Self-organizing maps (SOM) had been used for input data quantization and visual display of data, an important property that does not exist in most of clustering algorithms. Effective data clustering using SOM involves two or three steps procedure. After proper network training, units can be clustered generating regions of neurons which are related to data clusters. The basic assumption relies on the data density approximation by the neurons through unsupervised learning. This paper presents a gradient-based SOM visualization method and compares it with U-matrix. It also discusses steps toward clustering using SOM and morphological operators. Results using benchmark datasets show that the new method is more robust to choice of parameters in the filtering phase than the conventional method. The paper also proposes an enhancing method to map visualization taking advantage of the neurons activity, which improve cluster detection especially in small maps.