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(36)
Mining distancebased outliers in near linear time with randomization and a simple pruning rule
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Temporal sequence learning and data reduction for anomaly detection
Algorithms for Mining DistanceBased Outliers in Large Datasets
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DistanceBased Outliers: Algorithms and Applications
DistanceBased Outliers: Algorithms and Applications,10.1007/s007780050006,The Vldb Journal,Edwin M. Knorr,Raymond T. Ng,Vladimir Tucakov
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DistanceBased Outliers: Algorithms and Applications
(
Citations: 252
)
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Edwin M. Knorr
,
Raymond T. Ng
,
Vladimir Tucakov
This paper deals with finding outliers (excep tions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of pro fessional athletes. Existing methods that we have seen for finding outliers can only deal efficiently with two dimen sions/attributes of a dataset. In this paper, we study the no tion of DB (distancebased) outliers. Specifically, we show that (i)
outlier detection
can be done efficiently for large datasets, and for kdimensional datasets with large values of k (e.g., k 5); and (ii),
outlier detection
is a meaningful and important
knowledge discovery
task. First, we present two simple algorithms, both having a complexity of O( kN 2), k being the dimensionality and N being the number of objects in the dataset. These algorithms readily support datasets with many more than two attributes. Second, we present an optimized cellbased algorithm that has a complexity that is linear with respect to N , but expo nential with respect to k. We provide experimental results indicating that this algorithm significantly outperforms the two simple algorithms for k 4. Third, for datasets that are mainly diskresident, we present another version of the cell based algorithm that guarantees at most three passes over a dataset. Again, experimental results show that this algorithm is by far the best for k 4. Finally, we discuss our work on three reallife applications, including one on
spatiotemporal data
(e.g., a
video surveillance
application), in order to con firm the relevance and broad applicability of DB outliers.
Journal:
The Vldb Journal  VLDB
, vol. 8, no. 34, pp. 237253, 2000
DOI:
10.1007/s007780050006
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Citation Context
(156)
...
2008
)...
Liangxu Liu
,
et al.
An efficient outlying trajectories mining approach based on relative d...
...The problem of outlier detection has been studied extensively in the context of multidimensional data [7], [9], [
17
], [18]...
...These techniques are mostly either distancebased [
17
], [18] or densitybased [7], [9] methods...
Charu C. Aggarwal
,
et al.
Outlier detection in graph streams
...According to this definition, to detect distancebased outliers [3], [
4
] two parameters �� and �� are required, to control the density of each object’s neighborhood...
...Distancebased outliers is another simple and intuitive direction [3], [
4
], where an object is considered an outlier if there is a limited number of objects in its neighborhood...
Maria Kontaki
,
et al.
Continuous monitoring of distancebased outliers over data streams
...DistanceBased methods are one of the simplest and most popular outlier detection techniques [
26
], [27], [28]...
...Three definitions of distancebased outliers have been proposed as follows: 1) Outliers are those observations for which there are fewer than k observations within a distance d [
26
]...
Dina Said
,
et al.
Data preprocessing for distancebased unsupervised Intrusion Detection
...In this paper, we have used the definition of outliers as in [
30
] and [31]...
Kanishka Bhaduri
,
et al.
PrivacyPreserving Outlier Detection Through Random Nonlinear Data Dis...
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Citations
(252)
An efficient outlying trajectories mining approach based on relative distance
Liangxu Liu
,
Shaojie Qiao
,
Yongping Zhang
,
JinSong Hu
Journal:
International Journal of Geographical Information Science  GIS
, vol. aheadofp, no. aheadofp, pp. 122, 2012
Outlier detection in graph streams
(
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Charu C. Aggarwal
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)
Myounggyu Won
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Outlier detection by example
Cui Zhu
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Hiroyuki Kitagawa
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Statistical selection of relevant subspace projections for outlier ranking
Emmanuel Milller
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Thomas Seidl
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