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

The Group Method of Data Handling (GMDH) is a self-organizing data mining algorithm that optimizes prediction accuracy through iterative construction of complex models. Its core principle originates from the self-organizing characteristics of biological neural systems, achieving efficient data classification and prediction by filtering optimal combinations through multiple model layers. In implementation, GMDH typically involves recursive functions that generate and evaluate polynomial relationships between variables, with each layer's output serving as input for subsequent iterations.

Unlike traditional regression analysis, GMDH excels particularly in handling small-sample datasets, effectively avoiding overfitting issues common in conventional methods. The algorithm automatically selects optimal structures through iterative evaluation of different variable combinations, eliminating the need for manual predefinition of model forms, thereby enhancing generalization capabilities. From a coding perspective, this involves implementing fitness evaluation functions (often using criteria like regularity criterion or minimum description length) and dynamic model selection mechanisms that prune inferior candidates at each iteration stage.

The algorithm's core workflow comprises data partitioning, candidate model generation, and model optimization. At each layer, the best-performing intermediate models are selected as inputs for the next layer, ultimately building a high-precision prediction system. This hierarchical progressive approach, implemented through nested loops and selection algorithms, makes GMDH particularly effective in financial forecasting and industrial optimization applications. The code structure typically involves modular components for data preprocessing, layer-wise model generation, validation checks, and final model aggregation.