An adaptive outlier detection technique for data streams

Authors:
Shiblee Sadik;Le Gruenwald
Affiliations:
School of Computer Science, University of Oklahoma, Norman, OK;School of Computer Science, University of Oklahoma, Norman, OK
Venue:
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Year:
2011

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Abstract

This work presents an adaptive outlier detection technique for data streams, called Automatic Outlier Detection for Data Streams (A-ODDS), which identifies outliers with respect to all the received data points (global context) as well as temporally close data points (local context) where local context are selected based on time and change of data distribution.