An Unbiased Distance-based Outlier Detection Approach for High-dimensional Data

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  • Hoang Vu Nguyen, School of Computer Engineering, Nanyang Technological University, Singapore
  • Vivekanand Gopalkrishnan, School of Computer Engineering, Nanyang Technological University, Singapore
  • Ira Assent
  • Department of Computer Science
Traditional outlier detection techniques usually fail to work efficiently on high-dimensional data due to the curse of dimensionality. This work proposes a novel method for subspace outlier detection, that specifically deals with multidimensional spaces where feature relevance is a local rather than a global property. Different from existing approaches, it is not grid-based and dimensionality unbiased. Thus, its performance is impervious to grid resolution as well as the curse of dimensionality. In addition, our approach ranks the outliers, allowing users to select the number of desired outliers, thus mitigating the issue of high false alarm rate. Extensive empirical studies on real datasets show that our approach efficiently and effectively detects outliers, even in high-dimensional spaces.
Original languageEnglish
Book seriesLecture Notes in Computer Science
Volume6587
Pages (from-to)138-152
Number of pages15
ISSN0302-9743
DOIs
Publication statusPublished - 2011
Event16th International Conference on Database Systems for Advanced Applications (DASFAA) - Hong Kong, Hong Kong
Duration: 22 Apr 201125 Apr 2011

Conference

Conference16th International Conference on Database Systems for Advanced Applications (DASFAA)
CountryHong Kong
CityHong Kong
Period22/04/201125/04/2011

Bibliographical note

Title of the vol.: Database Systems for Advanced Applications (DASFAA). Proceedings, Part 1 / Jeffrey Xu Yu, Myoung Ho Kim and Rainer Unland (eds.)
ISBN: 978-3-642-20148-6

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