Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
SQL memory management in Oracle9i
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Cost-based query optimization for complex pattern mining on multiple databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Effective and efficient itemset pattern summarization: regression-based approaches
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating the number of frequent itemsets in a large database
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Approximating the number of frequent sets in dense data
Knowledge and Information Systems
Shaping SQL-Based frequent pattern mining algorithms
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Novel parallel method for mining frequent patterns on multi-core shared memory systems
DISCS-2013 Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems
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Frequent itemset counting is the first step for most association rule algorithms and some classification algorithms. It is the process of counting the number of occurrences of a set of items that happen across many transactions. The goal is to find those items which occur together most often. Expressing this functionality in RDBMS engines is difficult for two reasons. First, it leads to extremely inefficient execution when using existing RDBMS operations since they are not designed to handle this type of workload. Second, it is difficult to express the special output type of itemsets. In Oracle 10G, we introduce a new SQL table function which encapsulates the work of frequent itemset counting. It accepts the input dataset along with some user-configurable information, and it directly produces the frequent itemset results. We present examples of typical computations with frequent itemset counting inside Oracle 10G. We also describe how Oracle dynamically adapts during frequent itemset execution as a result of changes in the nature of the data as well as changes in the available system resources.