OUTLIER DETECTION AND VISUALISATION IN HIGH DIMENSIONAL DATA

Baya Lydia BOUDJELOUD, François POULET

2004

Abstract

The outlier detection problem has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. Such applications have to deal with high dimensional data sets with hundreds of dimensions. However, in high dimensional space, the data are sparse and the notion of proximity fails to retain its meaningfulness. Many recent algorithms use heuristics such as genetic algorithms, the taboo search... in order to palliate these difficulties in high dimensional data. We present in this paper a new hybrid algorithm for outlier detection in high dimensional data. We evaluate the performances of the new algorithm on different high dimensional data sets, and visualise its results for some data sets.

References

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Paper Citation


in Harvard Style

Lydia BOUDJELOUD B. and POULET F. (2004). OUTLIER DETECTION AND VISUALISATION IN HIGH DIMENSIONAL DATA . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 485-488. DOI: 10.5220/0002656004850488


in Bibtex Style

@conference{iceis04,
author={Baya Lydia BOUDJELOUD and François POULET},
title={OUTLIER DETECTION AND VISUALISATION IN HIGH DIMENSIONAL DATA},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2004},
pages={485-488},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002656004850488},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - OUTLIER DETECTION AND VISUALISATION IN HIGH DIMENSIONAL DATA
SN - 972-8865-00-7
AU - Lydia BOUDJELOUD B.
AU - POULET F.
PY - 2004
SP - 485
EP - 488
DO - 10.5220/0002656004850488