Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
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VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Scalable Mining for Classification Rules in Relational Databases
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
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In this paper, we report our success in building efficient scalable classifiers by exploring the capabilities of modern relational database management systems (RDBMS). In addition to high classification accuracy, the unique features of the approach include its high training speed, linear scalability, and simplicity in implementation. More importantly, the major computation required in the approach can be implemented using standard functions provided by the modern relational DBMS. Besides, with the effective rule pruning strategy, the algorithm proposed in this paper can produce a compact set of classification rules. The results of experiments conducted for performance evaluation and analysis are presented.