## Outlier detection for high dimensional data (2001)

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Citations: | 159 - 4 self |

### BibTeX

@INPROCEEDINGS{Aggarwal01outlierdetection,

author = {Charu C. Aggarwal},

title = {Outlier detection for high dimensional data},

booktitle = {},

year = {2001},

pages = {37--46}

}

### Years of Citing Articles

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### Abstract

The outlier detection problem has important applications in the eld of fraud detection, netw ork robustness analysis, and intrusion detection. Most suc h applications are high dimensional domains in whic hthe data can con tain hundreds of dimensions. Many recen t algorithms use concepts of pro ximity in order to nd outliers based on their relationship to the rest of the data. Ho w ever, in high dimensional space, the data is sparse and the notion of proximity fails to retain its meaningfulness. In fact, the sparsity of high dimensional data implies that every point is an almost equally good outlier from the perspective ofproximity-based de nitions. Consequently, for high dimensional data, the notion of nding meaningful outliers becomes substantially more complex and non-obvious. In this paper, w e discuss new techniques for outlier detection whic h nd the outliers by studying the behavior of projections from the data set. 1.

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Citation Context ...t then meaningful clusters cannot be found in the data [2, 3, 5, 11]; similarly it is difficult to detect abnormal deviations. For problems such as clustering and similarity search, it has been shown =-=[1, 2, 3, 5, 11, 19]-=- that by examining the behavior of the data in subspaces, it is possible to design more effective algorithms. This is because different localities of the data are dense with respect to different subse... |

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Citation Context ...ese methods do not work quite as well when the dimensionality is high and the data becomes sparse. Many data-mining algorithms in the literature nd outliers as a side-product of clustering algorithms =-=[2, 3, 5, 15, 18, 27]-=-. Ho w ever, these tec hniques de ne outliers as poin ts which do not lie in clusters. Th us,the techniques implicitly de ne outliers as the bac kground noise in whic hthe clusters are em bedded. Anot... |

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Identi cation of Outliers
- Hawkins
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Citation Context ...niques de ne outliers as poin ts which do not lie in clusters. Th us,the techniques implicitly de ne outliers as the bac kground noise in whic hthe clusters are em bedded. Another class of techniques =-=[7, 10, 13, 22, 23, 25]-=- de nes outliers as points whic h are neither a part of a Permission to make digital or hard copies of part or all of this work or personal or classroom use is granted without fee provided that copies... |