Applied multivariate statistical analysis
Applied multivariate statistical analysis
Self-organizing maps
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
PolSOM: A new method for multidimensional data visualization
Pattern Recognition
Yet another algorithm which can generate topography map
IEEE Transactions on Neural Networks
Two efficient connectionist schemes for structure preserving dimensionality reduction
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
Code vector density in topographic mappings: Scalar case
IEEE Transactions on Neural Networks
PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
A new Self-Organizing Map algorithm, called the probabilistic polar self-organizing map (PPoSOM), is proposed. PPoSOM is a new variant of PolSOM, which is constructed on 2-D polar coordinates. Two variables: radius and angle are used to reflect the data characteristics. PPoSOM, developed to enhance the visualization performance, provides more data characteristics compared with the traditional methods that use Euclidian distance as the only variable. The weight-updating rule of PPoSOM is associated with a cost function. Instead of using the hard assignment, PPoSOM employs the soft assignment that the assignment of data to neuron is based on a probabilistic function. The obtained results are compared with the conventional SOM and ViSOM. The presented results show that the proposed PPoSOM is an effective method for multidimensional data visualization. In addition, the quality measurement of mapping, synthetical cluster density (SCD) is applied and it shows PPoSOM exhibits an improved result compared with PolSOM.