AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets
- Submitting institution
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Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 168474306
- Type
- E - Conference contribution
- DOI
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10.1145/2939672.2939750
- Title of conference / published proceedings
- ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)
- First page
- 1025
- Volume
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- Issue
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- ISSN
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- Open access status
- -
- Month of publication
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- Year of publication
- 2016
- 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
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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C - Electrical and Electronic
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Here we provide a unique way to enhance the performance of DBSCAN, one of the most widely used data clustering algorithms used for real-world applications. Unlike all other state-of-the-art methods our approach can work under arbitrary resource constraints. Published in KDD 2014, the top conference in Data Mining (ranked by ERA (A), CORE (A*). AnyDBC is being incorporated into the opensource ELKI framework developed by the University of Munich, https://www.linuxlinks.com/elki/.
- Author contribution statement
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- Non-English
- No
- English abstract
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