Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
Mining fuzzy association rules in databases
ACM SIGMOD Record
Interesting Fuzzy Association Rules in Quantitative Databases
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Fuzzy Quantitative Association Rules
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
Association rule mining: models and algorithms
Association rule mining: models and algorithms
A framework for fuzzy quantification models analysis
IEEE Transactions on Fuzzy Systems
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
An empirical study of build maintenance effort
Proceedings of the 33rd International Conference on Software Engineering
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Association rule mining forms an important research area in the field of data mining. The theory of fuzzy sets can be used over relational databases to discover useful, meaningful patterns. In this paper, we propose an algorithm to mine fuzzy association rules over relational databases using Interest and Conviction measures. In the present work, we introduce fuzzy interest and fuzzy conviction measures and eliminate the rules, which have negative correlation. The experiments are conducted on an insurance database using our approach. The presented approach is very useful and efficient when there are more infrequent itemsets in a database.