Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30535
Title: Mining fuzzy association rules with weighted items
Authors: Shu, JY
Tsang, ECC
Yeung, DS
Shi, DM
Keywords: Data mining
Self-organising feature maps
Very large databases
Issue Date: 2000
Publisher: IEEE
Source: 2000 IEEE International Conference on Systems, Man, and Cybernetics, October 2000, Nashville, TN, v. 3, p. 1906-1911 How to cite?
Abstract: In most models of mining fuzzy association rules, the items are considered to have equal importance. Due to diverse human interest and preference for items, such models do not work well in many situations. To improve such models, we propose a method to mine fuzzy association rules with weighted items. One of the major problems in data mining research is the development of good measures of interest of discovered rules. The weighted support and weighted confidence for fuzzy association rules are defined. Kohonen self-organized mapping is used to fuzzify the numerical attributes into linguistic terms. A new fuzzy association rule mining algorithm, which generalizes the popular Apriori Gen large itemset based algorithm, is developed. The advantages of the new algorithm are shown by testing it on a census database with 5000 transaction records
URI: http://hdl.handle.net/10397/30535
ISBN: 0-7803-6583-6
ISSN: 1062-922X
DOI: 10.1109/ICSMC.2000.886391
Appears in Collections:Conference Paper

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