Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
A density-based cluster validity approach using multi-representatives
Pattern Recognition Letters
Introduction to Information Retrieval
Introduction to Information Retrieval
Approximate Spectral Clustering
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
A powerful method in knowledge discovery and cluster extraction is the use of self-organizing maps (SOMs), which provide adaptive quantization of the data together with its topologically ordered lower-dimensional representation on a rigid lattice. The knowledge extraction from SOMs is often performed interactively from informative visualizations. Even though interactive cluster extraction is successful, it is often time consuming and usually not straightforward for inexperienced users. In order to cope with the need of fast and accurate analysis of increasing amount of data, automated methods for SOM clustering have been popular. In this study, we use spectral clustering, a graph partitioning method based on eigenvector decomposition, for automated clustering of the SOM. Experimental results based on seven real data sets indicate that spectral clustering can successfully be used as an automated SOM segmentation tool, and it outperforms hierarchical clustering methods with distance based similarity measures.