A new Informative Generic Base of Association Rules
作者： Engelbert Mephu Nguifo , Yahya Slimani , Sadok Ben Yahia , Ghada Gasmi
DOI:
关键词: Galois connection 、 Data mining 、 Artificial intelligence 、 Computer science 、 Semantics (computer science) 、 Relevance (information retrieval) 、 Natural language processing 、 Axiomatic system 、 Context (language use) 、 Association rule learning 、 Base (topology)
摘要: The problem of the relevance and usefulness extracted association rules is becoming primary importance, since an overwhelm- ing number may be derived from even reasonably sized real-life databases. In this paper, we introduce a novel generic base rules, based on Galois connection semantics. sound informative. We also present axiomatic system, allowing to derive all that can drawn extraction context.
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