Databases Reference
In-Depth Information
outlier detection include Jin, Tung, and Han [JTH01]; Jin, Tung, Han, and Wang
[JTHW06]; and Papadimitriou, Kitagawa, Gibbons, et al. [PKG-F03]. The variations
differ in how they estimate density.
The bootstrap method discussed in Example 12.17 was developed by Barbara,
Li, Couto, et al. [BLC
C
03]. The FindCBOLF algorithm was given by He, Xu, and
Deng [HXD03]. For the use of fixed-width clustering in outlier detection meth-
ods, see Eskin, Arnold, and Prerau, et al. [EAP
C
02]; Mahoney and Chan [MC03];
and He, Xu, and Deng [HXD03]. Barbara, Wu, and Jajodia [BWJ01] used multiclass
classification in network intrusion detection.
Song, Wu, Jermaine, et al. [SWJR07] and Fawcet and Provost [FP97] presented a
method to reduce the problem of contextual outlier detection to one of conventional
outlier detection. Yi, Sidiropoulos, Johnson, Jagadish, et al. [YSJJ
C
00] used regres-
sion techniques to detect contextual outliers in co-evolving sequences. The idea in
Example 12.22 for collective outlier detection on graph data is based on Noble and Cook
[NC03].
The HilOut algorithm was proposed by Angiulli and Pizzuti [AP05]. Aggarwal and
Yu [AY01] developed the sparsity coefficient-based subspace outlier detection method.
Kriegel, Schubert, and Zimek [KSZ08] proposed angle-based outlier detection.
