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
Fast discovery of association rules
Advances in knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Rating the Interest of Rules Induced from Data and within Texts
DEXA '01 Proceedings of the 12th International Workshop on Database and Expert Systems Applications
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Visualization of attractive and repulsive zones between variables
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Quantitative and ordinal association rules mining (QAR mining)
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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Most rule-interest measures are suitable for binary attributes and using an unsupervised usual algorithm for the discovery of association rules requires a transformation for other kinds of attributes. Given that the complexity of these algorithms increases exponentially with the number of attributes, this transformation can lead us, on the one hand to a combinatorial explosion, and on the other hand to a prohibitive number of weakly significant rules with many redundancies. To fill the gap, we propose in this study a new objective rule-interest measure called intensity of inclination which evaluates the implication between two ordinal attributes (numeric or ordinal categorical attributes). This measure allows us to extract a new kind of knowledge : ordinal association rules. An evaluation of an application to some banking data ends up the study.