Self-Organizing Maps
Hierarchical overlapped SOM's for pattern classification
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
The growing hierarchical recurrent self organizing map for phoneme recognition
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
Network Load Predictions Based on Big Data and the Utilization of Self-Organizing Maps
Journal of Network and Systems Management
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The self-organizing map (SOM) is a very popular unsupervised neural-network model for analyzing of high-dimensional input data as in scientific data mining applications. However, to use the SOM, the network structure must be predetermined, this often leads constrains on potential applications. When the network is unfit to the data model, the resulting map will be of poor quality. In this paper, an intuitive and effective SOM is proposed for mapping high-dimensional data onto the two-dimensional SOM structure with a growing self-organizing map. In the training phase, an improved growing node structure is used. In the procedure of adaptive growing, the probability distribution of sample data is also a criterion to distinguish where the new nodes should to be added or deleted besides the maximal quantization error (mqe) of a unit. The improved method is demonstrated on a data set with promising results and a significantly reduced network size.