An algorithm to mine general association rules from tabular data

Ayubi, Siyamand; Muyeba, Maybin; Keane, John

Abstract

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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Intelligent Data Engineering and Automated Learning - IDEAL 2007 8th International Conference
• Start Date: Dec 16, 2007
• End Date: Dec 19, 2007
• Publication Date: 2007
• Deposit Date: Apr 7, 2025
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Pages: 705-717
• Series Number: 4881
• Book Title: Intelligent Data Engineering and Automated Learning - IDEAL 2007
• ISBN: 978-3-540-77225-5
• DOI: https://doi.org/10.1007/978-3-540-77226-2_71