Demchuk, Kostyantyn and Leith, Douglas J.
(2014)
A Fast Minimal Infrequent Itemset Mining Algorithm.
Working Paper.
arXiv.org.
(Submitted)
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: |
|
| 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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