The Components of Data Mining Algorithms

Reference: Fayyad et al. 1996

1. Model Representation is the language L for describing discoverable patterns.
2. Model Evaluation estimates how well a particular pattern (a model and its parameters) meet the criteria of the KDD process. Evaluation of predictive accuracy (validity) is based on cross validation. Evaluation of descriptive quality involves predictive accuracy, novelty, utility, and understandability of the fitted model. Both logical and statistical criteria can be used for model evaluation.
3. Search Method consists of two components:
1. In parameter search, the algorithm must search for the parameters which optimize the model evaluation criteria given observed data and a fixed model representation.
2. Model search occurs as a loop over the parameter search method: the model representation is changed so that a family of models are considered.

Reference: Fayyad et al. 1996

1. Decision Trees and Rules
2. Nonlinear Regression and Classification Methods
3. Example-based Methods
4. Probabilistic Graphical Dependency Models
5. Relational Learning Models