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 frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Discovery of Association Rules in Tabular Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Optimized Support Rules for Numeric Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Algorithms for mining association rules in bag databases
Information Sciences—Informatics and Computer Science: An International Journal
A trie-based APRIORI implementation for mining frequent item sequences
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The discovery of association rules from tabular databases comprising nominal and ordinal attributes
Intelligent Data Analysis
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Mining association rules is a major technique within data mining and has many applications. Most methods for mining association rules from tabular data mine simple rules which only represent equality in their items. Limiting the operator only to "=" results in many interesting frequent patterns that may exist not being identified. It is obvious that where there is an order between objects, greater than or less than a value is as important as equality. This motivates extension, from simple equality, to a more general set of operators. We address the problem of mining general association rules in tabular data where rules can have all operators {≤,,≠,=} in their antecedent part. The proposed algorithm, Mining General Rules (MGR), is applicable to datasets with discrete-ordered attributes and on quantitative discretized attributes. The proposed algorithm stores candidate general itemsets in a tree structure in such a way that supports of complex itemsets can be recursively computed from supports of simpler itemsets. The algorithm is shown to have benefits in terms of time complexity, memory management and has great potential for parallelization.