Comparing SOM neural network with fuzzy \(c\)-means, \(K\)-means and traditional hierarchical clustering algorithms

DOI10.1016/j.ejor.2005.03.039zbMath1103.62057OpenAlexW2020448260MaRDI QIDQ2503246

Sueli A. Mingoti, Joab O. Lima

Publication date: 14 September 2006

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2005.03.039

zbMATH Keywords

tableshierarchical clusteringfuzzy \(c\)-means\(K\)-means

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (7)

Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: review, taxonomy, research findings, challenges and future directions ⋮ Unnamed Item ⋮ A hierarchical hesitant fuzzy K-means clustering algorithm ⋮ Interest rate prediction: a neuro-hybrid approach with data preprocessing ⋮ BELMKN: Bayesian extreme learning machines Kohonen network ⋮ On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data ⋮ An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

Uses Software

• SAS
• SAS/STAT

Cites Work

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• Self-organization and associative memory
• Fuzzy clustering algorithms and their cluster validity
• Comparative performance of the FSCL neural net and \(K\)-means algorithm for market segmentation
• A comparison of SOM neural network and hierarchical clustering methods
• A study of the classification capabilities of neural networks using unsupervised learning: A comparison with \(K\)-means clustering
• Clustering incomplete relational data using the non-Euclidean relational fuzzy c-means algorithm
• Unresolved Problems in Cluster Analysis
• A Review of Hierarchical Classification
• A logical calculus of the ideas immanent in nervous activity
• Extending the Kohonen self-organizing map networks for clustering analysis.

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