Insights: Software that extracts knowledge from data. Model. Predict. Simulate. Discover. Associate. Gain new Knowledge and Insights from noisy data. Easily and Reliably. Ultra-fast, parallel, self-organizing, high-dimensional modeling of real-world processes and complex systems. INSIGHTS
The Theoretical Background of Insights.
Self-Organising Data Mining
Extracting Knowledge From Data
By Johann-Adolf Müller and Frank Lemke
ISBN 3-89811-861-4

A self-organising data mining creates optimal complex models systematically and autonomously by employing both parameter and structure identification. An optimal complex model is a model that optimally balances model quality on a given learning data set ("closeness of fit") and its generalisation power on new, not previously seen data with respect to the data's noise level and the task of modelling (prediction, classification, modelling, etc.). It thus solves the basic problem of experimental systems analysis of systematically avoiding "overfitted" models based on the data's information only. This makes self-organising data mining a most automated, fast and very efficient supplement and alternative to other data mining methods.

A PDF version of this book is included in the free download of Insights along with several other references.


On the Relation of Data Mining and Knowledge Mining
by Prof. A.G. Ivakhnenko

More Reading
The original GMDH site
Theoretical achievements and publications

Deep Learning and noise immunity of GMDH adaptive self-learning and self-organizing data mining, forecasting and classification of streams of data