Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 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
Optimizing Queries with Aggregate Views
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Including Group-By in Query Optimization
VLDB '94 Proceedings of the 20th 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
Eager Aggregation and Lazy Aggregation
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Optimal implementation of conjunctive queries in relational data bases
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
Data Mining Using Query Flocks with Views
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Index Support for Frequent Itemset Mining in a Relational DBMS
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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Data mining is rapidly finding its way into mainstream computing. The development of generic methods such as itemset counting has opened the area to academic inquiry and has resulted in a large harvest of research results. While the mined datasets are often in relational format, most mining systems do not use relational DBMS. Thus, they miss the opportunity to leverage the database technology developed in the last couple of decades. In this paper, we propose a data mining architecture, based on the query flock framework, that is tightly-coupled with RDBMS. To achieve optimal performance we transform a complex data mining query into a sequence of simpler queries that can be executed efficiently at the DBMS. We present a class of levelwise algorithms that generate such transformations for a large class of data mining queries. We also present some experimental results that validate the viability of our approach.