Mining Generalized Association Rules from Fuzzy Taxonomic Quantitative Databases

Mining association rules is a major aspect of data mining research In practice, there are multiple levels of abstraction （i e, taxonomic structure） among the attributes of the databases Srikant and Agrawal have proposed several algorithms to mine generalized Boolean association rules upon all levels of presumed crisp taxonomic structures However, in many real world applications, the taxonomic structures may not be exact but fuzzy This paper focuses on the issue of how to mine generalized association rules from quantitative database with fuzzy taxonomic structure, and a new fuzzy taxonomic quantitative database model is proposed to solve the problem Finally, the simulations verify the effectiveness of the new model