The Ilp Description Learning Problem: towards a General Model-level Deenition of Data Mining in Ilp

The task of discovering interesting regularities in (large) sets of data (data mining, knowledge discovery) has recently met with increased interest in Machine Learning in general and in Inductive Logic Programming (ILP) in particular. However, while there is a widely accepted deenition for the task of concept learning from examples in ILP, deenitions for the data mining task have been proposed only recently. In this paper, we examine these so-called "non-monotonic semantics" deenitions and show that non-monotonicity is only an incidental property of the data mining learning task, and that this task makes perfect sense without such an assumption. We therefore introduce and deene a generalized deenition of the data mining task called the ILP description learning problem and discuss its properties and relation to the traditional concept learning (prediction) learning problem. Since our characterization is entirely on the level of models, the deenition applies independently of the chosen hypothesis language.