Abstract
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.
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Brisson, L., Collard, M. (2009). How to Semantically Enhance a Data Mining Process?. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2008. Lecture Notes in Business Information Processing, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00670-8_8
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DOI: https://doi.org/10.1007/978-3-642-00670-8_8
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