An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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The discovery of the most recurrent association rules, in a large database of sales transactions requires that the sets of itens bought together by a sufficiently large population of customers are identified. This is a critical task, since the number of generated itemsets grows exponentially with the total number of items. Most of the algorithms start identifying the sets with the lowest cardinality, and subsequently, increase it progressively. Our approach is different, since the sets to be considered at a time are determined by the items in the sets. The main adventage is a significant reduction of the CPU time required to update data structures in main memory. This paper presents an algorithm that requires only one pass on the database, presents linear scale-up property with the dimensions of the database and, as shown by the experiments, performs better than other classical algorithms.