New Developments in Unsupervised Outlier Detection

Beschreibung

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.

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.

Presents algorithms for unsupervised outlier detection using k-nearest neighbor-based methods Proposes new global and local outlier factors that offer performance comparable to existing solutions Challenges and improves on traditional ideas in outlier detection Discusses an unconventional approach to multiple novel object detection applications

Autor*in

Xiaochun Wang

Themen in »New Developments in Unsupervised Outlier Detection«

Unsupervised Outlier Detection Distance-Based Outlier Detection Density-Based Outlier Detection k-Nearest Neighbors Based Outlier Detection Clustering Based Outlier Detection Boundary Point Detection Novel Object Detection

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