A Framework for Pattern-Based Global Model
Résumé
Discovering global models on a dataset (e.g., classifiers, clusterings, summaries) has attracted a lot of attention and many approaches can be found in the literature. However no framework has been proposed yet for describing and comparing these approaches in a uniform manner. In this paper we propose such a framework for pattern-based modeling approaches, i.e., approaches that use local patterns to construct a global model. This framework includes a generic algorithm (IGMA) for constructing a global model. We show that the framework allows to describe in an as declarative as possible way various different global model construction methods.