Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
On compressing frequent patterns
Data & Knowledge Engineering
Association rules mining in vertically partitioned databases
Data & Knowledge Engineering - Special issue: WIDM 2004
Association rules mining using heavy itemsets
Data & Knowledge Engineering
Mining frequent tree-like patterns in large datasets
Data & Knowledge Engineering
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Mining frequent patterns has been studied popularly in data mining research. Most of the current studies adopt an FP_growth-like approach which does not bring the candidate generation. However, the cost of recursively constructing each frequent item's conditional frequent pattern tree is high. In this paper, we propose a depth first algorithm for mining frequent patterns. Efficiency of mining is achieved with the following techniques: large database is compressed into a frequent pattern tree with a children table but not a header table, which avoids costly repeated database scans, on the other hand the mining algorithm adopts a depth first method which takes advantage of this tree structure and dynamically adjusts links instead of generating a lot of redundant sub trees, which can dramatically reduces the time and space needed for the mining process. The performance study shows that our algorithm is efficient and scalable for mining frequent patterns, and is an order of magnitude faster than Trie, FP_growth, H-mine and some recently reported new frequent patterns mining methods.