ECAI 2004 Conference Paper

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Rule-Mining: A Knowledge-based Selection of Association Rules

Dietmar Janetzko, Hacène Cherfi, Roman Kennke, Amedeo Napoli, Yannick Toussaint

A reoccuring problem in mining association rules is the selection of interesting association rules within the overall, and possibly huge set of extracted rules. The majority of previous works in this area uses statistical methods for quality estimation and selection of association rules. However, strictly bottom-up approaches are oblivious of knowledge though rule extraction may profit from the usage of knowledge. In this paper, we conceive of this problem as a classification task. The framework of a probabilistic knowledge-based classifier is introduced that uses ontologies in order to carry out the rule-mining task.

Keywords: Text Mining, Knowledge Discovery, Probabilistic Reasoning, Machine Learning, Natural Language Processing

Citation: Dietmar Janetzko, Hacène Cherfi, Roman Kennke, Amedeo Napoli, Yannick Toussaint: Rule-Mining: A Knowledge-based Selection of Association Rules. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.485-489.


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ECAI-2004 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Universitat Politècnica de València on behalf of Asociación Española de Inteligencia Artificial (AEPIA) and Associació Catalana d'Intel-ligència Artificial (ACIA).