Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
The structure of the information visualization design space
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Visualization and analysis of classifiers performance in multi-class medical data
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Two-level Clustering of Web Sites Using Self-Organizing Maps
Neural Processing Letters
DS '08 Proceedings of the 11th International Conference on Discovery Science
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
A self-organizing map (SOM) is a nonlinear, unsupervised neural network model that could be used for applications of data clustering and visualization. One of the major shortcomings of the SOM algorithm is the difficulty for non-expert users to interpret the information involved in a trained SOM. In this paper, this problem is tackled by introducing an enhanced version of the proposed visualization method which consists of three major steps: (1) calculating single-linkage inter-neuron distance, (2) calculating the number of data points in each neuron, and (3) finding cluster boundary. The experimental results show that the proposed approach has the strong ability to demonstrate the data distribution, inter-neuron distances, and cluster boundary, effectively. The experimental results indicate that the effects of visualization of the proposed algorithm are better than that of other visualization methods. Furthermore, our proposed visualization scheme is not only intuitively easy understanding of the clustering results, but also having good visualization effects on unlabeled data sets.