The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Fast vertical mining using diffsets
Abstract
A number of vertical mining algorithms have been proposed recently for association mining, which have shown to be very effective and usually outperform horizontal approaches. The main advantage of the vertical format is support for fast frequency counting via intersection operations on transaction ids (tids) and automatic pruning of irrelevant data. The main problem with these approaches is when intermediate results of vertical tid lists become too large for memory, thus affecting the algorithm scalability. In this paper we present a novel vertical data representation called Diffset, that only keeps track of differences in the tids of a candidate pattern from its generating frequent patterns. We show that diffsets drastically cut down the size of memory required to store intermediate results. We show how diffsets, when incorporated into previous vertical mining methods, increase the performance significantly. We also present a new algorithm, using diffsets, for mining maximal patterns. Experimental comparisons, on both dense and sparse databases, show that diffsets deliver order of magnitude performance improvements over the best previous methods. 1
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Cites
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Mining frequent patterns without candidate generation
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Self-citationAn Efficient Algorithm for Mining Association Rules in Large Databases
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An effective hash-based algorithm for mining association rules
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Cited by
Fast 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
Frequent substructure-based approaches for classifying chemical compounds
Deshpande, M., Kuramochi, M., Wale, N., & Karypis, G. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.127 · Cited by 371
Frequent itemset mining: A 25 years review
Luna, J. M., Fournier‐Viger, P., & Ventura, S. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1329 · Cited by 258
Metadata
- Authors
- Zaki, M. J., & Gouda, K.
- Venue
- Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2003-08-24
- Pages
- 326-335
- OpenAlex
- W2080632942
- Cited by
- 593
- References
- 24
- FWCI
- 30.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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