Predictive Data Mining: A Generalized Approach
In this paper, the authors included the ambitious task of formulating a general framework of data mining. They explained that the framework should fulfill. It should elegantly handle different types of data, different data mining tasks, and different types of patterns/models. They also discuss data mining languages and what they should support: this includes the design and implementation of data mining algorithms, as well as their composition into nontrivial multi step knowledge discovery scenarios relevant for practical application. They proceed by laying out some basic concepts, starting with (structured) data and generalizations (e.g., patterns and models) and continuing with data mining tasks and basic components of data mining algorithms (i.e., refinement operators, distances, features and kernels).