- 期刊
- OpenAccess
A Robust Scalable Model Using Hybrid Approach for the Detection of the Projected Outliers
並列摘要
The abnormal and anomalous observations even in the advanced technological era proves to be the biggest jolt to the concerned industry. To reduce and eliminate the outliers from the massive data streams, it is important to accurately highlight them from the higher dimensional data which is itself very challenging. In this study, a Scalable outlier detection model is proposed which is robust enough to resist and detect the projected outliers that are lying at some lower dimensional subspaces. This model exploits the problem of curse of dimensionality which is very frequent in large data streams and massive datasets. Rapid distance and density based approaches are used and then the probability density is measured by Gaussian Mixture Model. Baye's Probability is applied to the final observations so as confirm them as the projected outliers.
延伸閱讀
- 黃世傑（2022）。Developing Dynamic, Probabilistic, Quality Predictive Models: from One-Step Prediction to Multi-Step Predictions〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu202201317
- Blood, E. A., & Cheng, D. M. (2011). The Use of Mixed Models for the Analysis of Mediated Data with Time-Dependent Predictors. Journal of Environmental and Public Health, 2011(), 98-109. https://doi.org/10.1155/2011/435078
- Wang, Y. H., Dang, Y. G., & Liu, S. F. (2010). An Approach of Combining Optimization to Improve Prediction Precision of GM (1, 1) Model. Journal of Grey System, 13(3), 113-118. https://doi.org/10.30016/JGS.201009.0004
- Hwang, S. H., & Lin, M. L. (2002). A Method for Identification of Discrete Parametric Models with Unknown Orders and Delays. Journal of the Chinese Institute of Chemical Engineers, 33(4), 373-388. https://doi.org/10.6967/JCICE.200207.0373
- Chiu, S. C. (2011). A Robust Estimation Method for Outlier-Resistant in Functional Data Analysis [master's thesis, National Tsing Hua University]. Airiti Library. https://doi.org/10.6843/NTHU.2011.00450