Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9460
Title: Fuzzy taxonomy, quantitative database and mining generalized association rules
Authors: Wang, S
Chung, KFL 
Shen, H
Keywords: Data mining
Association rules
Fuzzy taxonomic quantitative database
Issue Date: 2005
Publisher: Ios Press
Source: Intelligent data analysis, 2005, v. 9, no. 2, p. 207-217 How to cite?
Journal: Intelligent Data Analysis 
Abstract: Mining association rules from databases is still a hot topic in data mining community in recent years. Due to the existence of multiple levels of abstraction (i.e, taxonomic structures) among the attributes of the databases, several algorithms were proposed to mine generalized Boolean association rules upon all levels of presumed crisp taxonomic structures. However, fuzzy taxonomic structures may be more suitable in many practical applications. In [ 9], the authors proposed an approach to mine generalized Boolean association rules with such fuzzy taxonomic structures. The main contribution of this paper is to extend their idea to mine generalized association rules from quantitative databases with fuzzy taxonomic structures. A new fuzzy taxonomic quantitative database model is presented, and the experimental results on realistic databases are demonstrated to validate this new model.
URI: http://hdl.handle.net/10397/9460
ISSN: 1088-467X
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