Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A fuzzy approach for mining quantitative association rules
Acta Cybernetica
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications
International Journal of Data Mining and Bioinformatics
A Framework for Mining Fuzzy Association Rules from Composite Items
New Frontiers in Applied Data Mining
Finding Associations in Composite Data Sets: The CFARM Algorithm
International Journal of Data Warehousing and Mining
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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.