Group Method of Data Handling was applied in a great variety of areas for data mining and knowledge discovery, forecasting and systems modeling, optimization and pattern recognition. Inductive GMDH algorithms give possibility to find automatically interrelations in data, to select optimal structure of model or network and to increase the accuracy of existing algorithms. This original self-organizing approach is substantially different from deductive methods used commonly for modeling. It has inductive nature – it finds the best solution by sorting-out of possible variants. By sorting of different solutions GMDH networks aims to minimize the influence of the author on the results of modeling. Computer itself finds the structure of the model and the laws which act in the system. Group Method of Data Handling is a set of several algorithms for different problems solution. It consists of parametric, clusterization, analogues complexing, rebinarization and probability algorithms. This has been added to Data Mining Resources Subject Tracer™ Information Blog and Knowledge Discovery Subject Tracer™ Information Blog.