Among many different methods belonging to data mining, the following can be distinguished: the general models of classification and regression trees (G_Trees), general CHAID (Chi-square Automatic Interaction Detection) models, interactive classification and regression trees (also with boosting – Boosted Trees), random forest, MARS (Multivariate Adaptive Regression Splines), artificial neural networks (ANN), other machine learning methods such as: naïve Bayes classifier (NBC), support vector machines (SVM), k-nearest neighbors (k-NN) and other regarded (or not) by different authors as data mining techniques.

The characteristic feature of these models is the use of a specific strategy, usually requiring the division of data into training set, sometimes also verification set, which enable the evaluation of the model quality as well as a test set for checking its prognostic or classification abilities.

However, they can also be used in a much narrower range, sometimes as an alternative to classical statistics. Data mining techniques involve mainly searching for various relationships in large data sets.

Id data mining and data dredging