Open Access
| Issue |
SHS Web Conf.
Volume 140, 2022
2022 International Conference on Information Technology in Education and Management Engineering (ITEME2022)
|
|
|---|---|---|
| Article Number | 01031 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/shsconf/202214001031 | |
| Published online | 25 May 2022 | |
- Huang Jingtao, Ren Zhiwei, Luo Wei. Research on Outlier Detection Algorithm of Power Station Boiler Monitoring Data [J]. Computers and Applied Chemistry 30(10), 1153-1156 (2013) [Google Scholar]
- Miao Runhua. Research on data preprocessing method based on clustering and outlier detection [D]. Beijing: Beijing Jiaotong University, (2012) [Google Scholar]
- Breunig M M, Kriegel H P, Ng R T, et al. LOF: identifying density-based local outliers[C]// ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA May 15-18, 2000, Oettingenstr, Munich, Germany: MDDT, 29(2), 93104 (2000) [Google Scholar]
- Tian Jiang. Research on Outlier Detection Method Based on Support Vector Machine [D]. Liaoning: Dalian University of Technology, (2009) [Google Scholar]
- Ma Y, Shi H, Ma H, et al. Dynamic process monitoring using adaptive local outlier factor[J]. Chemometrics & Intelligent Laboratory Systems, 127(18), 89-101 (2013) [CrossRef] [Google Scholar]
- Gao Z. Application of Cluster-Based Local Outlier Factor Algorithm in Anti-Money Laundering[C] International Conference on Management and Service Science, 1-4 (September 20-22, 2009, Wuhan, China: MSS, 2009) [Google Scholar]
- Vintrova V, Vintr T, Rezankova H. Comparison of Different Calculations of the Density-Based Local Outlier Factor[J], 60-67 (2012) [Google Scholar]
- Wang Fei. ILOF *: An Improved Local Anomaly Detection Algorithm [J]. Applications of Computer Systems, 24(12), 233-238 (2015) [Google Scholar]
- Xue Anrong, Yao Lin, Ju Nimui, et al. A Review of Outlier Mining Methods[J]. Computer Science, (11), 13-18, 27 (2008) [Google Scholar]
- Wang Qian, Liu Shuzhi. Improvement of Local outlier data Mining Method based on Density [J]. Application Research of Computers, 31(6), 1693-1696, 1701 (2014) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.