The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficiently mining maximal frequent itemsets
Abstract
We present GenMax, a backtracking search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. Systematic experimental comparison with previous work indicates that different methods have varying strengths and weaknesses based on dataset characteristics. We found GenMax to be a highly efficient method to mine the exact set of maximal patterns.
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Citations
Cites
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Cited by
Fast vertical mining using diffsets
Zaki, M. J., & Gouda, K. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956788 · Cited by 593
Self-citationFast algorithms for frequent itemset mining using FP-trees
Grahne, G., & Zhu, J. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.166 · Cited by 549
Metadata
- Authors
- Gouda, K., & Zaki, M. J.
- Venue
- Proceedings 2001 IEEE International Conference on Data Mining
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-14
- Pages
- 163-170
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2138660495
- Cited by
- 397
- References
- 1
- FWCI
- 21.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Gouda, K., & Zaki, M. J. (2002). Efficiently mining maximal frequent itemsets. Proceedings 2001 IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2001.989514
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