Analytical applications based on self-organizing maps
The unique self-organizing map (SOM) representation and visualization are powerful instruments for data modeling and exploration. However, the above mentioned visualization is just the starting point for much more extensive and in-depth data mining and predictive modeling.
Through the combination of the compact SOM data representation with the strength of classical statistics, Viscovery provides a unique approach to data analysis and predictive modeling which is unique in terms of intuition and effectiveness. The following have been chosen from a multitude of analytics capabilities to provide an overview of some prominent fields of application.
SOMs simplify clustering and allow the user to identify homogenous data groups visually. In Viscovery, several clustering algorithms (SOM Single Linkage, Ward, and SOM-Ward) are available for automatically building clusters.
Viscovery combines the non-linear data representation of the SOM with linear statistical prediction methods for each homogenous sub-group to improve prediction accuracy.
Data are highly compressed using statistical methods, allowing a single map that uses only a few megabytes of space to represent databases that are orders of magnitude larger.
New data can be located in the map extremely quickly — up to 100,000 previously unseen data records can be classified per second — allowing real-time assessment of new data.
Specific benefits of the Viscovery solution
Viscovery is the leading commercial solution for data mining applications based on SOMs. Advantages in terms of technological superiority include the following:
- Quick and concise model creation even for voluminous data sets
- Superb visualization of complex data and dependences
- Integration of conventional statistics with innovative methods of data representation
- Intuitive representation of abstract models and analysis results
- Integration of expert knowledge during the modeling process
- Outstanding prediction accuracy due to patented procedure for the extraction of non-linear relations
- Full workflow orientation