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New developments in unsupervised out...

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New developments in unsupervised outlier detectionalgorithms and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	New developments in unsupervised outlier detectionby Xiaochun Wang, Xiali Wang, Mitch Wilkes.
Reminder of title:	algorithms and applications /
Author:	Wang, Xiaochun.
other author:	Wang, Xiali.
Published:	Singapore :Springer Singapore :2021.
Description:	xxi, 277 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-981-15-9519-6
ISBN:	9789811595196$q(electronic bk.)

New developments in unsupervised outlier detectionalgorithms and applications /
Wang, Xiaochun.

New developments in unsupervised outlier detectionalgorithms and applications /[electronic resource] :by Xiaochun Wang, Xiali Wang, Mitch Wilkes. - Singapore :Springer Singapore :2021. - xxi, 277 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811595196$q(electronic bk.)

Standard No.: 10.1007/978-981-15-9519-6doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / W35 2021

Dewey Class. No.: 006.312

New developments in unsupervised outlier detectionalgorithms and applications /
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505 0 $a 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.
520 $a 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.
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