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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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