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 optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Discovery of Ordinal Association Rules
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Minimum variance associations: discovering relationships in numerical data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Association rules have exhibited an excellent ability to identify interesting association relationships among a set of binary variables describing huge amount of transactions. Although the rules can be relatively easily generalized to other variable types, the generalization can result in a computationally expensive algorithm generating a prohibitive number of redundant rules of little significance. This danger especially applies to quantitative and ordinal variables. This paper presents and verifies an alternative approach to the quantitative and ordinal association rule mining. In this approach, quantitative or ordinal variables are not immediately transformed into a set of binary variables. Instead, it applies simple arithmetic operations in order to construct the cedents and searches for areas of increased association which are finally decomposed into conjunctions of literals. This scenario outputs rules that do not syntactically differentiate from classical association rules.