Improve efficiency of fuzzy association rule using hedge algebra approach

Nguyễn Tuấn Anh, Trần Thái Sơn


A major problem when conducting mining fuzzy association rules from the database (DB) is the large computation time and memory needed. In addition, the selection of fuzzy sets for each attribute of the database is very important because it will affect the quality of the mining rule. This paper proposes a method for mining fuzzy association rules using the compressed database. We also use the approach of Hedge Algebra (HA) to build the membership function for attributes instead of using the normal way of fuzzy set theory. This approach allows us to explore fuzzy association rules through a relatively simple algorithm which is faster in terms of time, but it still brings association rules which are as good as the classical algorithms for mining association rules.


Data mining, association rules, compressed transactions, knowledge discovery, hedge algebras

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Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology