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
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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
A PC-NOW Based Parallel Extension for a Sequential DBMS
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
SQL Based Association Rule Mining Using Commercial RDBMS (IBM DB2 UBD EEE)
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
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Data mining has been widely recognized as a powerful tool to explore added value from large-scale databases. One of data mining techniques, generalized association rule mining with taxonomy, is potential to discover more useful knowledge than ordinary flat association mining by taking application specific information into account. We proposed SQL queries, named TTR-SQL and TH-SQL to perform this kind of mining and evaluated them on PC cluster. Those queries can be more than 30% faster than Apriori based SQL query reported previously. Although RDBMS has powerful query processing ability through SQL, most data mining systems use specialized implementations to achieve better performance. There is a tradeoff between performance and portability. Performance is not necessarily sufficiently high but seamless integration with existing RDBMS would be considerably advantageous. Since RDB is already very popular, the feasibility of generalized association rule mining can be explored using the proposed SQL query instead of purchasing expensive mining software. In addition, parallel RDB is now also widely accepted. We showed that paralleling the SQL execution can offer the same performance with those native programs with 10 to 15 nodes. Since most organizations have a lot of PCs, which are not fully utilized. We are able to exploit such resources to explore the performance significantly.