Fuzzy Association Rule Mining Techniques and Applications

Fuzzy Association Rule Mining (FARM) is considered as a significant part of data mining approach that uses fuzzy logic to extract meaningful patterns and relationship from large dataset. Fuzzy set are used to represent the membership degree of data in a particular set, which allows for more flexible and accurate modelling of data. The application of fuzzy association rule mining provides valuable insights and knowledge for decision-making in different domains. In this paper, basic concept of FARM and how to generate meaningful rules based on FARM algorithm is discussed. The issue of sharp boundary found in classical Association Rule Mining (ARM) technique can be solved using FARM technique. Different Membership Function can be used to convert crisp data into fuzzy data. Applying fuzzification method quantitative dataset is converted into fuzzy category dataset and binary category dataset. Then both dataset is implemented through different algorithms to generate rules and analyzed those rules.