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
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Mining Weighted Association Rules for Fuzzy Quantitative Items
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Basis of Fuzzy Knowledge Discovery System
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
On Classification and Regression
DS '98 Proceedings of the First International Conference on Discovery Science
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Data mining tries to discover interesting and surprising patterns among a given data set. An important task is to develop effective measures of interestingness for evaluating and ranking the discovered patterns. A good measure should give a high rank to patterns, which have strong evidence among data, but which yet are not too obvious. Thereby the initial set of patterns can be pruned before human inspection. In this paper we study interestingness measures for generalized quantitative association rules, where the attribute domains can be fuzzy. Several interestingness measures have been developed for the discrete case, and it turns out that many of them can be generalized to fuzzy association rules, as well. More precisely, our goal is to compare the fuzzy version of confidence to some other measures, which are based on statistics and information theory. Our experiments show that although the rankings of rules are relatively similar for most of the methods, also some anomalies occur. Our suggestion is that the information-theoretic measures are a good choice when estimating the interestingness of rules, both for fuzzy and non-fuzzy domains.