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Demchuk, Kostyantyn and Leith, Douglas J. (2014) A Fast Minimal Infrequent Itemset Mining Algorithm. Working Paper. arXiv.org. (Submitted)
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Abstract
A novel fast algorithm for finding quasi identifiers in large datasets is presented. Performance measurements
on a broad range of datasets demonstrate substantial reductions in run-time relative to the state of the art
and the scalability of the algorithm to realistically-sized datasets up to several million records.
| Item Type: | Monograph (Working Paper) |
|---|---|
| Additional Information: | Working paper submitted for publication in ACM Transactions on Knowledge Discovery from Data. |
| Keywords: | itemset mining; breadth-first algorithm; frequency-based analysis; k- anonymity; performance; load balancing; |
| Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 5951 |
| Identification Number: | arXiv:1403.6985 |
| Depositing User: | Professsor Douglas Leith |
| Date Deposited: | 11 Mar 2015 17:02 |
| Publisher: | arXiv.org |
| Refereed: | Yes |
| URI: | https://mu.eprints-hosting.org/id/eprint/5951 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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