New Developments in Unsupervised Outlier Detection - Algorithms and Applications (Hardcover, 1st ed. 2021)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	November 2020
First published:	2021
Authors:	Xiaochun Wang • Xiali Wang • Mitch Wilkes
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	277
Edition:	1st ed. 2021
ISBN-13:	978-981-15-9518-9
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	981-15-9518-6
Barcode:	9789811595189

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