Abstract
Data mining is becoming increasingly important since the size of databascs grows even larger and the need to explore hidden rules from the databases becomes widely recognized. Currently database systems arc dominated by relational database and the ability to perform data mining using standard SQL queries will definitely case 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. Keywords: data mining, parallel SQL, query optimization, commercial RDBMS
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References
R. Agrawal, T. Imielinski, A. Swami. Mining Association Rules between Sets of Items in Large Databases. In Proc. of the ACM SIGMOD Conference on Management of Data, 1993.
R. Agrawal, R. Strikant. Fast Algorithms for Mining Association Rules. In Proc. of the Very Large Database (VLDB) Conference, 1994.
M. Houtsma, A. Swami. Set-oriented Mining of Association Rules. In Proc. of International Conference on Data Engineering (ICDE), 1995.
Iko Pramudiono, Takahiko Shintani, Takayuki Tamura, Masaru Kitsuregawa. Parallel SQL Based Association Rule Mining on Large Scale PC Cluster: Performance Comparison with Directly Coded C Implementation. In Proc. of Third Pacific-Asia Conference on Knowledge Discovery a nd Data Mining (PAKDD99). 1999.
Iko Pramudiono, Takahiko Shintani, Takayuki Tamura, Masaru Kitsuregawa. Mining Generalized Association Rule using Parallel RDB Engine on PC Cluster. In Proc. of First International Conference on Data Warehousing and Knowledege Discovery (DAWAK99). 1999.
Jong Soo Park, Ming-Syan Chen, Philip S. Yu. An Effective Hash-Based Algorithm for Mining Association Rules. In Proc. of the ACM SIGMOD Conference on Management of Data, 1995.
Sunita Sarawagi, Shiby Thomas, Rakesh Agrawal. Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications. In Proc. of the ACM SIGMOD Conference on Management of Data, 1998.
Shiby Thomas, Sharma Chakravarthy. Performance Evaluation and Optimization of Join Queries for Association Rule Mining. In Proc. of First International Conference on Data Warehousing and Knowledege Discovery (DAWAK99), 1999.
T. Shintani, M. Kitsuregawa. Parallel Mining Algorithms for Generalized Association Rules with Classification Hierarchy. In Proc. of the ACM SIGMOD Conference on Management of Data, 1998.
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Yoshizawa, T., Pramudiono, I., Kitsuregawa, M. (2000). SQL Based Association Rule Mining Using Commercial RDBMS (IBM DB2 UDB EEE). In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2000. Lecture Notes in Computer Science, vol 1874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44466-1_30
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DOI: https://doi.org/10.1007/3-540-44466-1_30
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