From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Mining fuzzy association rules in databases
ACM SIGMOD Record
A fuzzy approach for mining quantitative association rules
Acta Cybernetica
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Parallel Fuzzy c-Means Clustering for Large Data Sets
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
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
Mining the Most Reliable Association Rules with Composite Items
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
Towards healthy association rule mining (HARM): a fuzzy quantitative approach
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Mining association rules with improved semantics in medical databases
Artificial Intelligence in Medicine
FAR-miner: a fast and efficient algorithm for fuzzy association rule mining
International Journal of Business Intelligence and Data Mining
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In this paper, a composite fuzzy association rule mining mechanism CFARM, directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using "properties" associated with these composite attributes. The exemplar application is the analysis of the nutrients contained in items found in grocery data sets. The paper commences with a review of the back ground and related work, and a formal definition of the CFARM concepts. The CFARM algorithm is then fully described and evaluated using both real and synthetic data sets.