GMDH Algorithm Source Code: Core Algorithm for Self-Organizing Data Mining with Robust Data Grouping Processing

The GMDH algorithm source code represents the fundamental algorithm for self-organizing data mining, exhibiting strong generalization capabilities. Compared to conventional regression analysis methods, it can effectively process small-sample datasets, making it highly applicable in practical implementations. The algorithm operates through iterative data grouping, where parameter estimation and predictions are performed based on inter-group interactions. This self-organizing approach enhances model accuracy and stability while maintaining strong performance even with large numbers of samples and features. Key implementation aspects include: - Iterative combinatorial selection of partial descriptions (polynomial neurons) - Automatic structure generation through multilayer networks - Objective external criteria for model validation - Threshold-based selection of optimal intermediate models Thus, the GMDH algorithm remains one of the most widely adopted methods in the data mining field, particularly valuable for complex systems modeling where traditional statistical methods face limitations.