New Developments in Unsupervised Outlier Detection Algorithms and Applications /

Chi tiết về thư mục
Những tác giả chính:	Wang, Xiaochun (Tác giả), Wang, Xiali (Tác giả), Wilkes, Mitch (Tác giả)
Tác giả của công ty:	SpringerLink (Online service)
Tóm tắt:	XXI, 277 p. 138 illus., 120 illus. in color. text
Ngôn ngữ:	Tiếng Anh
Được phát hành:	Singapore : Springer Nature Singapore : Imprint: Springer, 2021.
Phiên bản:	1st ed. 2021.
Những chủ đề:	Computational intelligence. Data mining. Engineering > Data processing. Computational Intelligence. Data Mining and Knowledge Discovery. Data Engineering.
Truy cập trực tuyến:	https://doi.org/10.1007/978-981-15-9519-6
Định dạng:	Điện tử eBook

Mục lục:

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. .

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