Mining association rules between sets of items in large databases

@article{Agrawal1993MiningAR,
  title={Mining association rules between sets of items in large databases},
  author={Rakesh Agrawal and Tomasz Imielinski and Arun N. Swami},
  journal={Proceedings of the 1993 ACM SIGMOD international conference on Management of data},
  year={1993},
  url={https://api.semanticscholar.org/CorpusID:490415}
}
  • R. AgrawalT. ImielinskiA. Swami
  • Published in SIGMOD Conference 1 June 1993
  • Computer Science
  • Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
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16 References

Database Mining: A Performance Perspective

The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented and an algorithm for classification obtained by combining the basic rule discovery operations is given.

An Interval Classifier for Database Mining Applications

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Knowledge Discovery in Databases

Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases, which spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets.

Classification and regression trees

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Scientific Discovery: Computational Explorations of the Creative Processes

Scientific Discovery: Computational Explorations of the Creative Processes. By Pat Langley, Herbert A. Simon, Gary L. Bradshaw and Jan M. Zytkow. Cambridge Mass., USA, The MIT Press, USD 13.50.

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