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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Discovering Associations with Uncertainty from Large Databases
verfasst von : Guoqing Chen, Peng Yan, Qiang Wei
Erschienen in: Recent Advances in Decision Making
Verlag: Springer Berlin Heidelberg
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Data mining, also known as knowledge discovery in databases, is the process of extracting desirable knowledge or interesting patterns from existing databases. As a specific form of knowledge, association reflects semantics in terms of relationships among attributes in databases, and has been widely studied recently. This chapter focuses on dealing with uncertainty in discovering association rules (AR) and functional dependencies (FD), and provides an overview of our efforts on association rules with fuzzy taxonomies (FAR), on implication-based fuzzy quantitative association rules (ARsi), and on functional dependencies with partial degrees of satisfaction (FD
d
).