A survey on unsupervised outlier detection in high‐dimensional numerical data

Statistical Analysis and Data Mining

Arthur Zimek¹  Erich Schubert²  HansPeter Kriegel³ 

[1] Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8;Institute for Informatics, Ludwig‐Maximilians Universitänchen, Germany;t Mü

关键词: curse of dimensionality;    anomalies in high‐;    dimensional data;    outlier detection in high‐;    dimensional data;    approximate outlier detection;    subspace outlier detection;    correlation outlier detection;   

DOI  :  10.1002/sam.11161

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Abstract High-dimensional data in Euclidean space pose special challenges to data mining algorithms. These challenges are often indiscriminately subsumed under the term `curse of dimensionality', more concrete aspects being the so-called `distance concentration effect', the presence of irrelevant attributes concealing relevant information, or simply efficiency issues. In about just the last few years, the task of unsupervised outlier detection has found new specialized solutions for tackling hi.

【 授权许可】

Unknown