A Partial-Closure Canonicity Test to Increase the Efficiency of CbO-Type Algorithms
- Submitting institution
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Sheffield Hallam University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1454
- Type
- E - Conference contribution
- DOI
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10.1007/978-3-319-08389-6_5
- Title of conference / published proceedings
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- First page
- 37
- Volume
- 8577
- Issue
- 8577
- ISSN
- 0302-9743
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
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-
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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0
- Research group(s)
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-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In-Close was adapted in the €3.5m EU ePOOLICE project (312651) to provide corroborated evidence of organised crime and in the €2.6m EU ATHENA project (313220) to cluster reports of incidents for crisis management. Sebastiao et al. (2017) used binary decision diagrams to improve the performance of In-Close for dense formal contexts. Kodagoda parallelised In-Close (2018). Makhalova et al. (2020) stated that “One of the most efficient algorithms from the CbO family is In-Close” and confirmed that the partial-closure canonicity test improved running times. Janostik et al. (2020) adopted pruning features of In-Close for their frequent itemset mining algorithm.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -