Fast memory efficient local outlier detection in data streams (extended abstract)

Mahsa Salehi*, Christopher Leckie, James C. Bezdek, Tharshan Vaithianathan, Xuyun Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference abstract

3 Citations (Scopus)


Outlier detection is an important task in data mining. With the growing need to analyze high speed data streams, the task of outlier detection becomes even more challenging as traditional outlier detection techniques can no longer assume that all the data can be stored for processing. While the well-known Local Outlier Factor (LOF) algorithm has an incremental version (called iLOF), it assumes unbounded memory to keep all previous data points. In this paper, we propose a memory efficient incremental local outlier (MiLOF) detection algorithm for data streams, and a more flexible version (MiLOF_F), both have an accuracy close to iLOF but within a fixed memory bound. In addition MiLOF_F is robust to changes in the number of data points, underlying clusters and dimensions in the data stream.

Original languageEnglish
Title of host publication2017 IEEE 33rd International Conference on Data Engineering (ICDE 2017)
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages2
ISBN (Electronic)9781509065431
Publication statusPublished - 2017
Externally publishedYes
EventIEEE 33rd International Conference on Data Engineering (ICDE) - San Diego, Canada
Duration: 19 Apr 201722 Apr 2017

Publication series

NameIEEE International Conference on Data Engineering
ISSN (Print)1084-4627


ConferenceIEEE 33rd International Conference on Data Engineering (ICDE)
CitySan Diego

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