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Outlier detection for high dimensional data
2001
Proceedings of the 2001 ACM SIGMOD international conference on Management of data - SIGMOD '01
The outlier detection problem has important applications in the eld of fraud detection, netw ork robustness analysis, and intrusion detection. Most such applications are high dimensional domains in whic hthe data can con tain hundreds of dimensions. Many recen t algorithms use concepts of pro ximit y in order to nd outliers based on their relationship to the rest of the data. Ho w ever, in high dimensional space, the data is sparse and the notion of proximity fails to retain its meaningfulness.
doi:10.1145/375663.375668
dblp:conf/sigmod/AggarwalY01
fatcat:oalq6xkqabfododas4mnkwiayy