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Demchuk, Kostiantyn (2014) A Fast Minimal Infrequent Itemset Mining Algorithm. Masters thesis, National University of Ireland Maynooth.
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Research Master Thesis by Kostiantyn Demchuk May 2014.pdf
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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: | Thesis (Masters) |
|---|---|
| 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: | 6325 |
| Depositing User: | IR eTheses |
| Date Deposited: | 04 Sep 2015 09:54 |
| Funders: | Science Foundation Ireland under Grant No. 11/PI/1177 |
| URI: | https://mu.eprints-hosting.org/id/eprint/6325 |
| 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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