New Developments in Unsupervised Outlier Detection Algorithms and Applications /

Xehetasun bibliografikoak
Egile Nagusiak:	Wang, Xiaochun (Egilea), Wang, Xiali (Egilea), Wilkes, Mitch (Egilea)
Erakunde egilea:	SpringerLink (Online service)
Gaia:	XXI, 277 p. 138 illus., 120 illus. in color. text
Hizkuntza:	ingelesa
Argitaratua:	Singapore : Springer Nature Singapore : Imprint: Springer, 2021.
Edizioa:	1st ed. 2021.
Gaiak:	Computational intelligence. Data mining. Engineering > Data processing. Computational Intelligence. Data Mining and Knowledge Discovery. Data Engineering.
Sarrera elektronikoa:	https://doi.org/10.1007/978-981-15-9519-6
Formatua:	Baliabide elektronikoa Liburua

Aurkibidea:

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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