Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A New Algorithm for Faster Mining of Generalized Association Rules
PKDD '98 Proceedings of the Second European Symposium 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
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Preknowledge-based generalized association rules mining
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Enumeration tree based emerging patterns mining by using two different supports
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
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Generalized association rule mining plays a very important role in Knowledge discovery in Databases (KDD). Generalized association rule mining is an extension of traditional association rule mining to discover more informative rules. In this paper, we describe a formal method for the problem of mining generalized association rules. In proposed method, The subset-superset and the parent-child relationships among generalized itemsets are introduced to present the different views of generalized itemsets, i.e. concept hierarchy. There are two phases in our proposed work; phases are "Level Defragmentation" and "Branch Defragmentation". Input of our algorithm is a conceptual hierarchy and a FP-tree. Using our proposed approaches, one can transform a lower level FP-tree to a Higher level FP-tree. Through higher level FP-tree we generate generalized association rule.