Piecewise Linear Projection Based on Self-Organizing Map
Neural Processing Letters
Visual exploration of production data using small multiples design with non-uniform color mapping
Computers and Industrial Engineering
Categorical data visualization and clustering using subjective factors
Data & Knowledge Engineering
2005 Special Issue: Cross-entropy embedding of high-dimensional data using the neural gas model
Neural Networks - 2005 Special issue: IJCNN 2005
Online data visualization using the neural gas network
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A web-based ERP data mining system for decision making
International Journal of Computer Applications in Technology
Combination of Vector Quantization and Visualization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A robust nonlinear projection method using the neural gas network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Visualizing asymmetric proximities with SOM and MDS models
Neurocomputing
Online data visualization of multidimensional databases using the hilbert space-filling curve
VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
Nonlinear projection using geodesic distances and the neural gas network
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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Dimensionality reducing mappings, often also denoted as multidimensional scaling, are the basis for multivariate data projection and visual analysis in data mining. Topology and distance preserving mapping techniques-e.g., Kohonen's self-organizing feature map (SOM) or Sammon's nonlinear mapping (NLM)-are available to achieve multivariate data projections for the following interactive visual analysis process. For large data bases, however, NLM computation becomes intractable. Also, if additional data points or data sets are to be included in the projection, a complete recomputation of the mapping is required. In general, a neural network could learn the mapping and serve for arbitrary additional data projection. However, the computational costs would also be high, and convergence is not easily achieved. In this work, a convenient hierarchical neural projection approach is introduced, where first an unsupervised neural network-e.g., a SOM-quantizes the data base, followed by fast NLM mapping of the quantized data. In the second stage of the hierarchy, an enhancement of the NLM by a recall algorithm is applied. The training and application of a second neural network, which is learning the mapping by function approximation, is quantitatively compared with this new approach. Efficient interactive visualization and analysis techniques, exploiting the achieved hierarchical neural projection for data mining, are presented