Comparison of Dynamic Itemset Mining Algorithms for Multiple Support Thresholds-Reference-Cited by-同舟云学术

Comparison of Dynamic Itemset Mining Algorithms for Multiple Support Thresholds

Published:2017 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 21st International Database Engineering & Applications Symposium on - IDEAS 2017
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Author:

Abuzayed Nourhan N.¹,Ergenç Belgin¹

Affiliation:

1. Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey

Publisher

ACM Press

Reference50 articles.

1. Y. Hu and Y. Chen. 2006. Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism. Decision Support Systems, 42(1), 1--24.

2. R. U. Kiran and P. K. Reddy. 2011. Novel Techniques to Reduce Search Space in Multiple Minimum Supports-Based Frequent Pattern Mining Algorithms. In The 14th International Conference on Extending Database Technology, ACM, New York, USA, 11--20.

3. D. W. Cheung, J. Han, V.T. Ng, and C.Y. Wong. 1996. Maintenance of discovered association rules in large databases. An incremental updating technique, In the 12th IEEE International Conference on Data Engineering, Upsala, Sweden, 106--114.

4. D. W. Cheung, S. D. Lee, and B. Kao. 1997. A general incremental technique for maintaining discovered association rules. In the 5th International Conference on Database Systems for Advanced Applications, Melbourne, Australia, 185--194.

5. D. Oğuz and B. Ergenç. 2012. Incremental Itemset Mining Based on Matrix Apriori. In DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery, Vienna, Austria, 192--204.

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