How to semantically enhance a data mining process?

Laurent Brisson (1) , Martine Collard (2)

1 LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information
2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

This paper presents the KEOPS data mining methodology centered on domain knowledge integration. KEOPS is a CRISP-DM compliant methodology which integrates a knowledge base and an ontology. In this paper, we focus first on the pre-processing steps of business understanding and data understanding in order to build an ontology driven information system (ODIS). Then we show how the knowledge base is used for the post-processing step of model interpretation. We detail the role of the ontology and we define a part-way interestingness measure that integrates both objective and subjective criteria in order to eval model relevance according to expert knowledge. We present experiments conducted on real data and their results.

Mots clés: Knowledge integration; Ontology driven information system; Data mining

Domaines: Ordinateur et société [cs.CY]

Laurent Brisson, Martine Collard. How to semantically enhance a data mining process?. Enterprise information systems, 19, Springer Berlin Heidelberg, pp.103 - 116, 2009, Lecture Notes in Business Information Processing, 978-3-642-00669-2. ⟨10.1007/978-3-642-00670-8_8⟩. ⟨hal-02121061⟩