Enhancing Outlier Detection by Filtering Out Core Points and Border Points
Outlier detection is an important task in data mining and has high practical value in numerous applications such as astronomical observation, text detection, fraud detection, and so on. At present, a large number of popular outlier detection algorithms ar
- PDF / 10,077,469 Bytes
- 287 Pages / 453.543 x 683.15 pts Page_size
- 14 Downloads / 283 Views
ew Developments in Unsupervised Outlier Detection Algorithms and Applications
New Developments in Unsupervised Outlier Detection
Xiaochun Wang · Xiali Wang · Mitch Wilkes
New Developments in Unsupervised Outlier Detection Algorithms and Applications
Xiaochun Wang School of Software Engineering Xi’an Jiaotong University Xi’an, Shaanxi, China
Xiali Wang School of Information Engineering Chang’an University Xi’an, Shaanxi, China
Mitch Wilkes Department of Electrical Engineering and Computer Science Vanderbilt University Nashville, TN, USA
ISBN 978-981-15-9518-9 ISBN 978-981-15-9519-6 (eBook) https://doi.org/10.1007/978-981-15-9519-6 Jointly published with Xi’an Jiaotong University Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Xi’an Jiaotong University Press. © Xi’an Jiaotong University Press 2021 This work is subject to copyright. All rights are reserved by the Publishers, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Foreword
Being an active research topic in data mining, outlier detection aims to discover observations in a dataset that deviate from other observations so much as to arouse suspicions that they are generated by a different mechanism and is of utmost importance in many application domains. Unsupervised outlier detection plays a crucial role in the outlier detection research and sets out enormous theoretical and applied challenges to advanced data mining technology using unsupervised learning techniques. This monograph addresses unsupervised outlier detection in a local setting of k-nearest neighborhood. Unlike traditional distribution-based outlier detection techniques, k-nearest neighbor-based outlier detection approaches, typified
Data Loading...
