New Developments in Unsupervised Outlier Detection Algorithms and Applications /
| Главные авторы: | , , |
|---|---|
| Автор-организация: | |
| Примечания: | XXI, 277 p. 138 illus., 120 illus. in color. text |
| Язык: | английский |
| Опубликовано: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2021.
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| Издание: | 1st ed. 2021. |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1007/978-981-15-9519-6 |
| Формат: | Электронный ресурс eКнига |
Оглавление:
- Overview and Contributions
- Developments in Unsupervised Outlier Detection Research
- A Fast Distance-Based Outlier Detection Technique Using A Divisive Hierarchical Clustering Algorithm
- A k-Nearest Neighbour Centroid Based Outlier Detection Method
- A Minimum Spanning Tree Clustering Inspired Outlier Detection Technique
- A k-Nearest Neighbour Spectral Clustering Based Outlier Detection Technique
- Enhancing Outlier Detection by Filtering Out Core Points and Border Points
- An Effective Boundary Point Detection Algorithm via k-Nearest Neighbours Based Centroid
- A Nearest Neighbour Classifier Based Automated On-Line Novel Visual Percept Detection Method
- Unsupervised Fraud Detection in Environmental Time Series Data. .