On compiling queries in recursive first-order databases
Journal of the ACM (JACM)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Database management systems
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: building competitive advantage
Data mining: building competitive advantage
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Integrating Data Mining with Relational DBMS: A Tightly-Coupled Approach
NGIT '99 Proceedings of the 4th International Workshop on Next Generation Information Technologies and Systems
| Hi-index | 0.00 |
Data Mining is the process of finding trends and patterns in large data. Association rule mining become one of the most important techniques for extracting useful information such as regularities in the historical data. Query flocks extends the concept of association rule mining with a "generate-and-test" model for many different kind of patterns. This paper further extends the query flocks with view definitions. Also, a new data mining architecture simply compiles the query flocks from datalog to SQL. On this architecture, optimizations suitable for the extended query flocks are introduced. The prototype of the system is developed on a commercial database environment. Advantages of the new design and the extension to the query flocks, together with the optimizations, are also presented.