AnyOut : Anytime Outlier Detection Approach for High-dimensional Data

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearchpeer-review

Standard

AnyOut : Anytime Outlier Detection Approach for High-dimensional Data. / Assent, Ira; Kranen, Philipp; Baldauf, Corinna ; Seidl, Thomas.

In: Lecture Notes in Computer Science, Vol. 7238, 2012, p. 228-242.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearchpeer-review

Harvard

Assent, I, Kranen, P, Baldauf, C & Seidl, T 2012, 'AnyOut : Anytime Outlier Detection Approach for High-dimensional Data', Lecture Notes in Computer Science, vol. 7238, pp. 228-242. https://doi.org/10.1007/978-3-642-29038-1_18

APA

Assent, I., Kranen, P., Baldauf, C., & Seidl, T. (2012). AnyOut : Anytime Outlier Detection Approach for High-dimensional Data. Lecture Notes in Computer Science, 7238, 228-242. https://doi.org/10.1007/978-3-642-29038-1_18

CBE

Assent I, Kranen P, Baldauf C, Seidl T. 2012. AnyOut : Anytime Outlier Detection Approach for High-dimensional Data. Lecture Notes in Computer Science. 7238:228-242. https://doi.org/10.1007/978-3-642-29038-1_18

MLA

Vancouver

Assent I, Kranen P, Baldauf C, Seidl T. AnyOut : Anytime Outlier Detection Approach for High-dimensional Data. Lecture Notes in Computer Science. 2012;7238:228-242. https://doi.org/10.1007/978-3-642-29038-1_18

Author

Assent, Ira ; Kranen, Philipp ; Baldauf, Corinna ; Seidl, Thomas. / AnyOut : Anytime Outlier Detection Approach for High-dimensional Data. In: Lecture Notes in Computer Science. 2012 ; Vol. 7238. pp. 228-242.

Bibtex

@inproceedings{e828671acc61429b91264dbdfa2d7bd9,
title = "AnyOut : Anytime Outlier Detection Approach for High-dimensional Data",
abstract = "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",
author = "Ira Assent and Philipp Kranen and Corinna Baldauf and Thomas Seidl",
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",
year = "2012",
doi = "10.1007/978-3-642-29038-1_18",
language = "English",
volume = "7238",
pages = "228--242",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer",

}

RIS

TY - GEN

T1 - AnyOut : Anytime Outlier Detection Approach for High-dimensional Data

AU - Assent, Ira

AU - Kranen, Philipp

AU - Baldauf, Corinna

AU - Seidl, Thomas

N1 - 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

PY - 2012

Y1 - 2012

N2 - 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

AB - 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

U2 - 10.1007/978-3-642-29038-1_18

DO - 10.1007/978-3-642-29038-1_18

M3 - Conference article

VL - 7238

SP - 228

EP - 242

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -