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
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Efficient Mining for Association Rules with Relational Database Systems
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
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Although knowledge discovery from large relational databases has gained popularity, and its significance is well recognized, the prohibitive nature of the cost associated with extracting such knowledge, and the lack of suitable declarative query language support, still act as limiting factors. Surprisingly, little or no relational technology has yet been significantly exploited in data mining even though data often reside in relational tables. Consequently, no relational optimization has yet been possible for data mining. We exploit the transitive nature of large item sets and the so called anti-monotonicity property of support thresholds of large item sets to develop a natural least fixpoint operator for data mining. The operator proposed has several advantages including optimization opportunities, and traditional candidate set free large item set generation. We present an SQL3 expression for association rule mining and discuss its mapping to the least fixpoint operator developed in this paper, and thereby establish the equivalence of the top-down and bottom-up computation of large item sets in relational databases.