First we introduce the terms model and parameter . A model is a description of the state of some data given in a language or as a graphical representation, for instance as the structure of a Bayesian network. The parameters for a model are data for the model that can be learned from a database, for instance for a Bayesian network, the parameters are the probabilities on the edges. Then we can decompose the Data Mining algorithms into:
We distinguish between supervised data mining, where the user has to identify a specific goal or weigh features before the operation can begin, and unsupervised data mining, which does not require any interaction with the user.
Below, a method for Data Mining which will be used in illustrating integration of Data Mining and Case-Based Reasoning is outlined.