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Comparing SOM neural network with fuzzy $$c$$-means, $$K$$-means and traditional hierarchical clustering algorithms. (English) Zbl 1103.62057
Summary: 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 networks 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.

MSC:
 62H30 Classification and discrimination; cluster analysis (statistical aspects) 68T05 Learning and adaptive systems in artificial intelligence
SAS; SAS/STAT
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References:
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