AnyOut : Anytime Outlier Detection Approach for High-dimensional Data

Research output: Research - peer-reviewConference article

  • Ira Assent
  • Philipp Kranen
    Philipp KranenData Management and Data Exploration Group, RWTH Aachen UniversityGermany
  • Corinna Baldauf
    Corinna BaldaufData Management and Data Exploration Group, RWTH Aachen UniversityGermany
  • Thomas Seidl
    Thomas SeidlData Management and Data Exploration Group, RWTH Aachen UniversityGermany
With the increase of sensor and monitoring applications, data mining on streaming data is receiving increasing research attention. As data is continuously generated, mining algorithms need to be able to analyze the data in a one-pass fashion. In many applications the rate at which the data objects arrive varies greatly. This has led to anytime mining algorithms for classification or clustering. They successfully mine data until the a priori unknown point of interruption by the next data in the stream.

In this work we investigate anytime outlier detection. Anytime outlier detection denotes the problem of determining within any period of time whether an object in a data stream is anomalous. The more time is available, the more reliable the decision should be. We introduce AnyOut, an algorithm capable of solving anytime outlier detection, and investigate different approaches to build up the underlying data structure. We propose a confidence measure for AnyOut that allows to improve the performance on constant data streams. We evaluate our method in thorough experiments and demonstrate its performance in comparison with established algorithms for outlier detection
Original languageEnglish
Book seriesLecture Notes in Computer Science
Volume7238
Pages (from-to)228-242
Number of pages15
ISSN0302-9743
DOIs
StatePublished - 2012
EventInternational Conference on Database Systems for Advanced Applications - Busan, Korea, Republic of
Duration: 15 Apr 201219 Apr 2012
Conference number: 17

Conference

ConferenceInternational Conference on Database Systems for Advanced Applications
Number17
CountryKorea, Republic of
CityBusan
Period15/04/201219/04/2012

Bibliographical note

Title of the vol.: 17th International Conference, DASFAA 2012, Busan, South Korea, April 15-19, 2012, Proceedings, Part I/ eds.: Sang-goo Lee, Zhiyong Peng, Xiaofang Zhou, Yang-Sae Moon, Rainer Unland, Jaesoo Yoo
ISBN: 978-3-642-29037-4, 978-3-642-29038-1

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