Abstract. Most knowledge discovery processes are biased since some part of the knowledge structure must be given before extraction. We propose a framework that avoids this bias by supporting all major model structures e.g. clustering, sequences, etc., as well as specifications of data and DM (Data Mining) algorithms, in the same language. A unification operation is provided to match automatically the data to the relevant DM algorithms in order to extract models and their related structure. The MDL principle is used to evaluate and rank models. This evaluation is based on the covering relation that links the data to the models. The notion of schema, related to the category theory, is the key concept of our approach. Intuitively, a schema is an algebraic specification enhanced by the union of types, and the concepts of list and relation. An example based on network alarm mining illustrates the process.
For instance, Vautier et al. REF proposed a framework which is based on an algebraic specification of pattern mining operators.
Towards data mining without information on knowledge structure
in &quot;PKDD’07 (Principles and Practice of Knowledge Discovery in Databases
Computer Science