Self-organizing maps in mining gene expression data
Information Sciences: an International Journal
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Neural Networks - 2005 Special issue: IJCNN 2005
Challenges in Visual Data Analysis
IV '06 Proceedings of the conference on Information Visualization
Advanced visualization of self-organizing maps with vector fields
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Visual cluster analysis of trajectory data with interactive Kohonen maps
Information Visualization
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Visualising class distribution on self-organising maps
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Advanced visualization techniques for self-organizing maps with graph-based methods
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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In recent years, a variety of visualization techniques for visual data exploration based on self-organizing maps (SOMs) have been developed. To support users in data exploration tasks, a series of software tools emerged which integrate various visualizations. However, the focus of most research was the development of visualizations which improve the support in cluster identification. In order to provide real insight into the data set it is crucial that users have the possibility of interactively investigating the data set. This work provides an overview of state-of-the-art software tools for SOM-based visual data exploration. We discuss the functionality of software for specialized data sets, as well as for arbitrary data sets with a focus on interactive data exploration.