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 |
Appears in Collections: | Journal/Magazine Article |
Show full item record
WEB OF SCIENCETM
Citations
13
Last Week
0
0
Last month
1
1
Citations as of Feb 18, 2019
Page view(s)
80
Last Week
0
0
Last month
Citations as of Feb 17, 2019

Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.