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 frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
CBAR: an efficient method for mining association rules
Knowledge-Based Systems
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The taxonomy(is-a hierarchy) data exists widely in retail, geography, biology and financial area, so mining the multilevel and generalized association rules is one of the most important research task in data mining. Unlike the traditional algorithm, which is based on Apriori method, we propose a new CBP (correlation based partition) based method, to mine the multilevel and generalized frequent itemsets. This method uses the item's correlation as measurement to partition the transaction database from top to bottom. It can shorten the time of mining multilevel and generalized frequent itemsets by reducing the scanning scope of the transaction database. The experiments on the real-life financial transaction database show that the CBP based algorithms outperform the well-known Apriori based algorithms.