Applied multivariate techniques
Applied multivariate techniques
Self-organizing maps
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Generalized part family formation through fuzzy self-organizing feature map neural network
Computers and Industrial Engineering
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Self-organizing feature map for cluster analysis in multi-disease diagnosis
Expert Systems with Applications: An International Journal
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
SOM++: integration of self-organizing map and k-means++ algorithms
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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In data analysis techniques, the capability of SOM and K-means for clustering large-scale databases has already been confirmed. The most remarkable advantage of SOM-based two-stage methods is in saving time without the considerable computations required by conventional clustering methods for large and complicated data sets. In this research, we propose and evaluate a two-stage clustering method, which combines an ant-based SOM and K-means. The ant-based SOM clustering model, ABSOM, embeds the exploitation and exploration rules of state transition into the conventional SOM algorithm to avoid falling into local minima. After application to four practical data sets, the ABSOM itself not only performs better than Kohonen's SOM but also it works very well in the two-stage clustering analysis when it is taken as the preprocessing technique for the ABSOM+K-means method.