Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Association Rule Extraction for Text Mining
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
Comparing association rules and decision trees for disease prediction
HIKM '06 Proceedings of the international workshop on Healthcare information and knowledge management
Customer pattern search for after-sales service in manufacturing
Expert Systems with Applications: An International Journal
Indexing ICD-9 codes for free-textual clinical diagnosis records by a new ensemble classifier
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Non-classical logic in an intelligent assessment sub-system
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Evaluating association rules and decision trees to predict multiple target attributes
Intelligent Data Analysis
Reliable representations for association rules
Data & Knowledge Engineering
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CDVE'06 Proceedings of the Third international conference on Cooperative Design, Visualization, and Engineering
A knowledge-based clinical toxicology consultant for diagnosing single exposures
Artificial Intelligence in Medicine
Finding Associations in Composite Data Sets: The CFARM Algorithm
International Journal of Data Warehousing and Mining
On Sharp Boundary Problem in Rule Based Expert Systems in the Medical Domain
International Journal of Healthcare Information Systems and Informatics
Discovering frequent pattern pairs
Intelligent Data Analysis
Compass: A hybrid method for clinical and biobank data mining
Journal of Biomedical Informatics
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The discovery of new knowledge by mining medical databases is crucial in order to make an effective use of stored data, enhancing patient management tasks. One of the main objectives of data mining methods is to provide a clear and understandable description of patterns held in data. We introduce a new approach to find association rules among quantitative values in relational databases. The semantics of such rules are improved by introducing imprecise terms in both the antecedent and the consequent, as these terms are the most commonly used in human conversation and reasoning. The terms are modeled by means of fuzzy sets defined in the appropriate domains. However, the mining task is performed on the precise data. These ''fuzzy association rules'' are more informative than rules relating precise values. We also introduce a new measure of accuracy, based on Shortliffe and Buchanan's certainty factors [Shortliffe E, Buchanan B. Math Biosci 1975;23:351-79]. Also, the semantics of the usual measure of usefulness of an association rule, called support are discussed and some new criteria are introduced. Our new measures have been shown to be more understandable and appropriate than ordinary ones. Several experiments on large medical databases show that our new approach can provide useful knowledge with better semantics in this field.