Association Rule Mining with Fuzzy Logic: an Overview

One of the new mining technique is generated by a combination of association rule mining and fuzzy logic is Fuzzy association rule mining (Fuzzy ARM). An association relationship can help in decision making for the solution of a given problem. Association Rule Mining (ARM) with fuzzy logic concept facilitates the straightforward process of mining of latent frequent or repeated patterns supported their own frequencies within the sort of association rules from any transactional and relational datasets containing items to indicate the foremost recent trends in the given dataset. These fuzzy association rules use either for physical data analysis or additionally influenced to compel any mining tasks like categorization (classification) and collecting (clustering) which helps domain area experts to automate decision-making. Within the concept of data mining, usually fuzzy Association Rule Mining (FARM) technique has been comprehensively adopted in transactional and relational datasets those datasets containing items who has a fewer to medium quantity of attributes/dimensions. Classical association rule mining uses the concept of crisp sets. AS it uses crisp sets, classical association rule mining has number of drawbacks. To conquer drawbacks of classical association rule, the concept of fuzzy association rule mining is introduced. There is an enormous range of various sorts of fuzzy association rule mining algorithms are available for research works and day by day these algorithms are getting better. However at identical time problem domain also becoming more complex in nature so that research work is still going on continuously. In this paper, I have studied several well-known methodologies and algorithms for fuzzy association rule mining.