%0 Journal Article
	%A Chien-Hua Wang and  Chin-Tzong Pang
	%D 2009
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 29, 2009
	%T Finding Fuzzy Association Rules Using FWFP-Growth with Linguistic Supports and Confidences
	%U https://publications.waset.org/pdf/15457
	%V 29
	%X In data mining, the association rules are used to search
for the relations of items of the transactions database. Following the
data is collected and stored, it can find rules of value through
association rules, and assist manager to proceed marketing strategy
and plan market framework. In this paper, we attempt fuzzy partition
methods and decide membership function of quantitative values of
each transaction item. Also, by managers we can reflect the
importance of items as linguistic terms, which are transformed as
fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth
(FWFP-Growth) is used to complete the process of data mining. The
method above is expected to improve Apriori algorithm for its better
efficiency of the whole association rules. An example is given to
clearly illustrate the proposed approach.
	%P 1414 - 1422