Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 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
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Database System Concepts
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Integrating Data Mining with SQL Databases: OLE DB for Data Mining
Proceedings of the 17th International Conference on Data Engineering
Ad Hoc Association Rule Mining as SQL3 Queries
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Integration of Data Mining with Database Technology
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining First-order Knowledge Bases for Association Rules
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
Efficient Mining for Association Rules with Relational Database Systems
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
A Logic-Based Approach to Mining Inductive Databases
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Reducing the size of databases for multirelational classification: a subgraph-based approach
Journal of Intelligent Information Systems
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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, as well as the lack of suitable declarative query language support 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 set oriented data mining from relational databases. 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.