Self-Organizing Maps
On Clustering Validation Techniques
Journal of Intelligent Information Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A Methodology Using Neural Network to Cluster Validity Discovered from a Marketing Database
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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Determining the number of clusters has been one of the most difficult problems in data clustering. The Self-Organizing Map (SOM) has been widely used for data visualization and clustering. The SOM can reduce the complexity in terms of computation and noise of input patterns. However, post processing steps are needed to extract the real data structure learnt by the map. One approach is to use other algorithm, such as K-means, to cluster neurons. Finding the best value of K can be aided by using an cluster validity index. On the other hand, graph-based clustering has been used for cluster analysis. This paper addresses an alternative methodology using graph theory for SOM clustering. The Davies-Bouldin index is used as a cluster validity to analyze inconsistent neighboring relations between neurons. The result is a segmented map, which indicates the number of clusters as well as the labeled neurons. This approach is compared with the traditional approach using K-means. The experimental results using the approach addressed here with three different databases presented consistent results of the expected number of clusters.