Frequent itemset mining in big data with effective single scan algorithms
- Submitting institution
-
University of the West of England, Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5203047
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2018.2880275
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 68013
- Volume
- 6
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Deposit exception
- Month of publication
- November
- Year of publication
- 2018
- URL
-
https://doi.org/10.1109/ACCESS.2018.2880275
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces the first single scan approach for frequent itemset mining. In addition to the accurate algorithm, heuristics and parallel implementation are proposed for sparse and big databases. The work is significant since the proposed algorithms require only one scan and always generate a fixed number of itemsets, which is useful for many applications. Numerical results show clear improvement vs. the state-of-the-art approaches. This work is related to an international collaboration with HIT, China (NSFC grant 61503092), HVL, Norway. It opened perspectives to further collaboration projects and joint publications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
