Self-organizing maps
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Visual Explorations in Finance
Visual Explorations in Finance
Clustering validity checking methods: part II
ACM SIGMOD Record
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On the Use of Self-Organizing Maps for Clustering and Visualization
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Integration of Neural Networks and Knowledge-Based Systems in Medicine
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
An aggregated clustering approach using multi-ant colonies algorithms
Pattern Recognition
Improved SOM learning using simulated annealing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A Quick Assessment of Topology Preservation for SOM Structures
IEEE Transactions on Neural Networks
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The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input spaces. Fast Learning SOM (FLSOM) adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. In this paper we show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate the superiority of the algorithm in comparison with the original SOM.