Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms [An article from: European Journal of Operational Research]

This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
In this paper we present a comparison among some nonhierarchical and hierarchical clustering algorithms including SOM (Self-Organization Map) neural network and Fuzzy c-means methods. Data were simulated considering correlated and uncorrelated variables, nonoverlapping and overlapping clusters with and without outliers. A total of 2530 data sets were simulated. The results showed that Fuzzy c-means had a very good performance in all cases being very stable even in the presence of outliers and overlapping. All other clustering algorithms were very affected by the amount of overlapping and outliers. SOM neural network did not perform well in almost all cases being very affected by the number of variables and clusters. The traditional hierarchical clustering and K-means methods presented similar performance.