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New Developments in Unsupervised Outlier Detection

Algorithms and Applications

Specificaties
Paperback, blz. | Engels
Springer Nature Singapore | e druk, 2021
ISBN13: 9789811595219
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Juridisch :
Springer Nature Singapore e druk, 2021 9789811595219
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research.
The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.

Specificaties

ISBN13:9789811595219
Taal:Engels
Bindwijze:paperback
Uitgever:Springer Nature Singapore

Inhoudsopgave

<div>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.&nbsp;<br></div>

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        New Developments in Unsupervised Outlier Detection