Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Parallel mining algorithms for generalized association rules with classification hierarchy
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
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 Rule Using Parallel RDB Engine on PC Cluster
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Performance Evaluation and Optimization of Join Queries for Association Rule Mining
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
On a fuzzy group-by and its use for fuzzy association rule mining
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Programming relational databases for Itemset mining over large transactional tables
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
SQL based frequent pattern mining with FP-Growth
INAP'04/WLP'04 Proceedings of the 15th international conference on Applications of Declarative Programming and Knowledge Management, and 18th international conference on Workshop on Logic Programming
Shaping SQL-Based frequent pattern mining algorithms
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Novel parallel method for mining frequent patterns on multi-core shared memory systems
DISCS-2013 Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems
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Data mining is becoming increasingly important since the size of databases grows even larger and the need to explore hidden rules from the databases becomes widely recognized. Currently database systems are dominated by relational database and the ability to perform data mining using standard SQL queries will definitely ease implementation of data mining. However the performance of SQL based data mining is known to fall behind specialized implementation and expensive mining tools being on sale. In this paper we present an evaluation of SQL based data mining on commercial RDBMS (IBM DB2 UDB EEE). We examine some techniques to reduce I/O cost by using View and Subquery. Those queries can be more than 6 times faster than SETM SQL query reported previously. In addition, we have made performance evaluation on parallel database environment and compared the performance result with commercial data mining tool (IBM Intelligent Miner). We prove that SQL based data mining can achieve sufficient performance by the utilization of SQL query customization and database tuning.