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
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 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
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
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Finding Association Rules with Some Very Frequent Attributes
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Partitioning strategies for distributed association rule mining
The Knowledge Engineering Review
Trend mining in social networks: a study using a large cattle movement database
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Frequent pattern trend analysis in social networks
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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
Computing frequent itemsets in parallel using partial support trees
PVM/MPI'05 Proceedings of the 12th European PVM/MPI users' group conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Temporal approach to association rule mining using t-tree and p-tree
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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The problem of extracting all association rules from within a binary database is well-known. Existing methods may involve multiple passes of the database, and cope badly with densely- packed database records because of the combinatorial explosion in the number of sets of attributes for which incidence-counts must be computed. We describe here a class of methods we have introduced that begin by using a single database pass to perform a partial computation of the totals required, storing these in the form of a set enumeration tree, which is created in time linear to the size of the database. Algorithms for using this structure to complete the count summations are discussed, and a method is described, derived from the well-known Apriori algorithm. Results are presented demonstrating the performance advantage to be gained from the use of this approach.