Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms

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...

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Veröffentlicht in:European journal of operational research Jg. 174; H. 3; S. 1742 - 1759
Hauptverfasser: Mingoti, Sueli A., Lima, Joab O.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.11.2006
Elsevier
Elsevier Sequoia S.A
Schriftenreihe:European Journal of Operational Research
Schlagworte:
Algorithms
Applied sciences
Artificial intelligence
Clustering
Comparative analysis
Computer science; control theory; systems
Connectionism. Neural networks
Exact sciences and technology
Fuzzy c-means
Fuzzy logic
Hierarchical clustering
Hierarchies
K-means
Multivariate statistics
Neural networks
SOM neural network
Studies
K-means
SOM neural network
Multivariate statistics
Hierarchical clustering
Fuzzy c-means
Fuzzy logic
Self-organising feature maps
Hierarchical classification
K means algorithm
Neural network
Fuzzy neural nets
ISSN:0377-2217, 1872-6860
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Beschreibung
Zusammenfassung: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.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2005.03.039