Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Complexity analysis of depth first and FP-growth implementations of APRIORI
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Complexity analysis of depth first and FP-growth implementations of APRIORI
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Anomaly extraction in backbone networks using association rules
IEEE/ACM Transactions on Networking (TON)
Mining frequent itemsets over tuple-evolving data streams
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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We examine the complexity of Depth First and FP-growth implementations of Apriori, two of the fastest known data mining algorithms to find frequent itemsets in large databases. We describe the algorithms in a similar style, derive theoretical formulas, and provide experiments on both synthetic and real life data to illustrate the theory.