Abstract:

Data mining has become an essential area of research allowing to exact useful and meaningful knowledge from data. Association rule is one of the most important data mining techniques. It is able to discover association relationships that help in decision making. Several algorithms were proposed in literature. Those algorithms deal only with binary data. They suffer from several drawbacks such a sharp boundary problem. Fuzzy association rules were introduced in order to treat quantitative data using Fuzzy set. A large number of algorithms were proposed to mine fuzz association rules. In this paper, we propose an overview of different algorithms of fuzzy association rules mining then we compare them in order to identify the current drawbacks to solve in future works.