Towards Healthy Association Rule Mining (HARM): A Fuzzy Quantitative Approach

Association Rule Mining (ARM) is a popular data mining technique that has been used to determine customer buying patterns. Although improving performance and efficiency of various ARM algorithms is important, determining Healthy Buying Patterns (HBP) from customer transactions and association rules is also important. This paper proposes a framework for mining fuzzy attributes to generate HBP and a method for analysing healthy buying patterns using ARM. Edible attributes are filtered from transactional input data by projections and are then converted to Required Daily Allowance (RDA) numeric values. Depending on a user query, primitive or hierarchical analysis of nutritional information is performed either from normal generated association rules or from a converted transactional database. Query and attribute representation can assume hierarchical or fuzzy values respectively. Our approach uses a general architecture for Healthy Association Rule Mining (HARM) and prototype support tool that implements the architecture. The paper concludes with experimental results and discussion on evaluating the proposed framework.