Abstract
Mining association rules and the relative knowledge from databases has been a focused topic in recent data mining fields. This paper focuses on the issue of how to mine generalized association rules from quantitative databases with fuzzy taxonomic structure, and a new fuzzy taxonomic quantitative database model has been proposed to solve the problem. The new model is demonstrated effective on a real-world databases.
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Shen, Hb., Wang, St., Yang, J. (2004). Fuzzy Taxonomic, Quantitative Database and Mining Generalized Association Rules. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_75
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DOI: https://doi.org/10.1007/978-3-540-25929-9_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
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