An approach for mining fuzzy association rules based on hedge algebra

Abstract

Fuzzy association rules based on fuzzy set theory have been researched by many authors and have published many significant results and flexibility in data mining with uncertain information. However, the approach using fuzzy set theory for the problem of mining fuzzy association rules still has certain limitations. In this paper, we propose a method for mining fuzzy association rules based on hedge algebra with each linguistic value represented by a their neighborhood. The hedge algebra has advantage of using the measure functions and semantic quantifier functions, the problem of mining fuzzy association rules and calculating them is quite simply and intuitively. The results obtained after extracting fuzzy association rules on the survey dataset of freshman in choosing a university are a channel providing useful information for university managers in enrollment communication.