Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using the new DB2: IBM's object-relational database system
Using the new DB2: IBM's object-relational database system
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PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 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
SQL open heterogeneous data access
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Compression Support in Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SchemaSQL - A Language for Interoperability in Relational Multi-Database Systems
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Monet And Its Geographic Extensions: A Novel Approach to High Performance GIS Processing
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Bottom-Up Association Rule Mining in Relational Databases
Journal of Intelligent Information Systems - Special issue on data warehousing and knowledge discovery
On the Equivalence of Top-Down and Bottom-Up Data Mining in Relational Databases
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Memory-adative association rules mining
Information Systems - Databases: Creation, management and utilization
Embedded predictive modeling in a parallel relational database
Proceedings of the 2006 ACM symposium on Applied computing
On a fuzzy group-by and its use for fuzzy association rule mining
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
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With the tremendous growth of large-scale data repositories, a need for integrating the exploratory techniques of data mining with the capabilities of relational systems to efficiently handle large volumes of data has now risen. In this paper, we look at the performance of the most prevalent association rule mining algorithm - Apriori, with IBM's DB2 Universal Database system. We show that a multi-column (MC) data model is preferable over the commonly used single column (SC) data model for association rule mining. We obtain factors of 4.8 to 6 improvement in performance for the MC data model over commercial implementations for the SC data model. We provide a new relational operator, called Combinations, for efficient SQL implementation of Apriori in the database engine - this results in trivial parallelizability, reliability, and portability for the mining application.