Chapter 1
OUTLIER DETECTION
Irad Ben-Gal
Department of Industrial Engineering
Tel-Aviv University
Ramat-Aviv, Tel-Aviv 69978, Israel.
bengal@eng.tau.ac.il
Abstract Outlier detection is a primary step in many data-mining applications. We present
several methods for outlier detection, while distinguishing between univariate
vs. multivariate techniques and parametric vs. nonparametric procedures. In
presence of outliers, special attention should be taken to assure the robustness of
the used estimators. Outlier detection for data mining is often based on distance
measures, clustering and spatial methods.
Keywords: Outliers, Distance measures, Statistical Process Control, Spatial data
1. Introduction: Motivation, Definitions and Applications
In many data analysis tasks a large number of variables are being recorded
or sampled. One of the first steps towards obtaining a coherent analysis is the
detection of outlaying observations. Although outliers are often considered
as an error or noise, they may carry important information. Detected outliers
are candidates for aberrant data that may otherwise adversely lead to model
misspecification, biased parameter estimation and incorrect results. It is therefore
important to identify them prior to modeling and analysis (Williams et al.,
2002; Liu et al., 2004).
An exact definition of an outlier often depends on hidden assumptions regarding
the data structure and the applied detection method. Yet, some definitions
are regarded general enough to cope with various types of data and
methods. Hawkins (Hawkins, 1980) defines an outlier as an observation that
deviates so much from other observations as to arouse suspicion that it was
generated by a different mechanism. Barnet and Lewis (Barnett and Lewis,
1994) indicate that an outlying observation, or outlier, is one that appears to
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
2
deviate markedly from other members of the sample in which it occurs, similarly,
Johnson (Johnson, 1992) defines an outlier as an observation in a data
set which appears to be inconsistent with the remainder of that set of data.
Other case-specific definitions are given below.
Outlier detection methods have been suggested for numerous applications,
such as credit card fraud detection, clinical trials, voting irregularity analysis,
data cleansing, network intrusion, severe weather prediction, geographic information
systems, athlete performance analysis, and other data-mining tasks
(Hawkins, 1980; Barnett and Lewis, 1994; Ruts and Rousseeuw, 1996; Fawcett
and Provost, 1997; Johnson et al., 1998; Penny and Jolliffe, 2001; Acuna and
Rodriguez, 2004; Lu et al., 2003).
2. Taxonomy of Outlier Detection Methods
Outlier detection methods can be divided between univariate methods, proposed
in earlier works in this field, and multivariate methods that usually form
most of the current body of research. Another fundamental taxonomy of outlier
detection methods is between parametric (statistical) methods and nonparametric
methods that are model-free (e.g., see (Williams et al., 2002)). Statistical
parametric methods either assume a known underlying distribution of
the observations (e.g., (Hawkins, 1980; Rousseeuw and Leory, 1987; Barnett
and Lewis, 1994)) or, at least, they are based on statistical estimates of unknown
distribution parameters (Hadi, 1992; Caussinus and Roiz, 1990). These
methods flag as outliers those observations that deviate from the model assumptions.
They are often unsuitable for high-dimensional data sets and for
arbitrary data sets without prior knowledge of the underlying data distribution
(Papadimitriou et al., 2002).
Within the class of non-parametric outlier detection methods one can set
apart the data-mining methods, also called distance-based methods. These
methods are usually based on local distance measures and are capable of handling
large databases (Knorr and Ng, 1997; Knorr and Ng, 1998; Fawcett
and Provost, 1997; Williams and Huang, 1997; Mouchel and Schonlau, 1998;
Knorr et al., 2000; Knorr et al., 2001; Jin et al., 2001; Breunig et al., 2000;
Williams et al., 2002; Hawkins et al., 2002; Bay and Schwabacher, 2003). Another
class of outlier detection methods is founded on clustering techniques,
where a cluster of small sizes can be considered as clustered outliers (Kaufman
and Rousseeuw, 1990; Ng and Han, 1994; Ramaswamy et al., 2000; Barbara
and Chen, 2000; Shekhar and Chawla, 2002; Shekhar and Lu, 2001; Shekhar
and Lu, 2002; Acuna and Rodriguez, 2004). Hu and Sung (Hu and Sung,
2003), whom proposed a method to identify both high and low density pattern
clustering, further partition this class to hard classifiers and soft classifiers.
The former partition the data into two non-overlapping sets: outliers and nonBen-Gal
I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
Outlier Detection 3
outliers. The latter offers a ranking by assigning each datum an outlier classification
factor reflecting its degree of outlyingness. Another related class of
methods consists of detection techniques for spatial outliers. These methods
search for extreme observations or local instabilities with respect to neighboring
values, although these observations may not be significantly different from
the entire population (Schiffman et al., 1981; Ng and Han, 1994; Shekhar and
Chawla, 2002; Shekhar and Lu, 2001; Shekhar and Lu, 2002; Lu et al., 2003).
Some of the above-mentioned classes are further discussed bellow. Other
categorizations of outlier detection methods can be found in (Barnett and Lewis,
1994; Papadimitriou et al., 2002; Acuna and Rodriguez, 2004; Hu and Sung,
2003).
3. Univariate Statistical Methods
Most of the earliest univariate methods for outlier detection rely on the assumption
of an underlying known distribution of the data, which is assumed
to be identically and independently distributed (i.i.d.). Moreover, many discordance
tests for detecting univariate outliers further assume that the distribution
parameters and the type of expected outliers are also known (Barnett and
Lewis, 1994). Needless to say, in real world data-mining applications these
assumptions are often violated.
A central assumption in statistical-based methods for outlier detection, is a
generating model that allows a small number of observations to be randomly
sampled from distributions G1,..., Gk, differing from the target distribution
F, which is often taken to be a normal distribution N µ, σ2 (see (Ferguson,
1961; David, 1979; Barnett and Lewis, 1994; Gather, 1989; Davies and Gather,
1993)). The outlier identification problem is then translated to the problem of
identifying those observations that lie in a so-called outlier region. This leads
to the following definition (Davies and Gather, 1993):
For any confidence coefficient α, 0 < α < 1, the α-outlier region of the
N µ, σ2 distribution is defined by
out α, µ, σ2
= x : |x − µ| > z1−α/2σ , (1.1)
where zq is the q quintile of the N(0,1). A number x is an α-outlier with respect
to F if x ∈ out α,µ,σ2 . Although traditionally the normal distribution has
been used as the target distribution, this definition can be easily extended to
any unimodal symmetric distribution with positive density function, including
the multivariate case.
Note that the outlier definition does not identify which of the observations
are contaminated, i.e., resulting from distributions G1,..., Gk , but rather it
indicates those observations that lie in the outlier region.
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
4
3.1 Single-step vs. Sequential Procedures
Davis and Gather (Davies and Gather, 1993) make an important distinction
between single-step and sequential procedures for outlier detection. Singlestep
procedures identify all outliers at once as opposed to successive elimination
or addition of datum. In the sequential procedures, at each step, one
observation is tested for being an outlier.
With respect to Equation 1.1, a common rule for finding the outlier region
in a single-step identifier is given by
out αn, ˆµn, ˆσ2
n = {x : |x − ˆµn| > g (n, αn) ˆσn} , (1.2)
where n is the size of the sample; ˆµn and ˆσn are the estimated mean and standard
deviation of the target distribution based on the sample; αn denotes the
confidence coefficient following the correction for multiple comparison tests;
and g (n, αn) defines the limits (critical number of standard deviations) of the
outlier regions.
Traditionally, ˆµn, ˆσn are estimated respectively by the sample mean, ¯xn, and
the sample standard deviation, Sn. Since these estimates are highly affected by
the presence of outliers, many procedures often replace them by other, more robust,
estimates that are discussed in Section . The multiple-comparison correction
is used when several statistical tests are being performed simultaneously.
While a given α-value may be appropriate to decide whether a single observation
lies in the outlier region (i.e., a single comparison), this is not the case for
a set of several comparisons. In order to avoid spurious positives, the α-value
needs to be lowered to account for the number of performed comparisons. The
simplest and most conservative approach is the Bonferroni’s correction, which
sets the α-value for the entire set of n comparisons equal to α, by taking the
α-value for each comparison equal to α/n. Another popular and simple correction
uses αn = 1 − (1 − α)1/n
. Note that the traditional Bonferroni’s method
is ”quasi-optimal” when the observations are independent, which is in most
cases unrealistic. The critical value g (n, αn) is often specified by numerical
procedures, such as Monte Carlo simulations for different sample sizes (e.g.,
(Davies and Gather, 1993)).
3.2 Inward and Outward Procedures
Sequential identifiers can be further classified to inward and outward procedures.
In inward testing, or forward selection methods, at each step of the
procedure the “most extreme observation”, i.e., the one with the largest outlyingness
measure, is tested for being an outlier. If it is declared as an outlier, it
is deleted from the dataset and the procedure is repeated. If it is declared as a
non-outlying observation, the procedure terminates. Some classical examples
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
Outlier Detection 5
for inward procedures can be found in (Hawkins, 1980; Barnett and Lewis,
1994).
In outward testing procedures, the sample of observations is first reduced
to a smaller sample (e.g., by a factor of two), while the removed observations
are kept in a reservoir. The statistics are calculated on the basis of the reduced
sample and then the removed observations in the reservoir are tested in reverse
order to indicate whether they are outliers. If an observation is declared as
an outlier, it is deleted from the reservoir. If an observation is declared as a
non-outlying observation, it is deleted from the reservoir, added to the reduced
sample, the statistics are recalculated and the procedure repeats itself with a
new observation. The outward testing procedure is terminated when no more
observations are left in the reservoir. Some classical examples for inward procedures
can be found in (Rosner, 1975; Hawkins, 1980; Barnett and Lewis,
1994).
The classification to inward and outward procedures also applies to multivariate
outlier detection methods.
3.3 Univariate Robust Measures
Traditionally, the sample mean and the sample variance give good estimation
for data location and data shape if it is not contaminated by outliers. When
the database is contaminated, those parameters may deviate and significantly
affect the outlier-detection performance.
Hampel (Hampel, 1971; Hampel, 1974) introduced the concept of the breakdown
point, as a measure for the robustness of an estimator against outliers.
The breakdown point is defined as the smallest percentage of outliers that can
cause an estimator to take arbitrary large values. Thus, the larger breakdown
point an estimator has, the more robust it is. For example, the sample mean has
a breakdown point of 1/nsince a single large observation can make the sample
mean and variance cross any bound. Accordingly, Hampel suggested the
median and the median absolute deviation (MAD) as robust estimates of the
location and the spread. The Hampel identifier is often found to be practically
very effective (Perarson, 2002; Liu et al., 2004). Another earlier work that
addressed the problem of robust estimators was proposed by Tukey (Tukey,
1977). Tukey introduced the Boxplot as a graphical display on which outliers
can be indicated. The Boxplot, which is being extensively used up to date, is
based on the distribution quadrants. The first and third quadrants, Q1 and Q3,
are used to obtain the robust measures for the mean, ˆµn = (Q1 + Q3)/2, and
the standard deviation, ˆσn = Q3 − Q1. Another popular solution to obtain robust
measures is to replace the mean by the median and compute the standard
deviation based on (1–α) percents of the data points, where typically α¡5%.
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
6
Liu et al. (Liu et al., 2004) proposed an outlier-resistant data filter-cleaner
based on the earlier work of Martin and Thomson (Martin and Thomson,
1982). The proposed data filter-cleaner includes an on-line outlier-resistant
estimate of the process model and combines it with a modified Kalman filter to
detect and “clean” outliers. The proposed method does not require an apriori
knowledge of the process model. It detects and replaces outliers on-line while
preserving all other information in the data. The authors demonstrated that
the proposed filter-cleaner is efficient in outlier detection and data cleaning for
autocorrelated and even non-stationary process data.
3.4 Statistical Process Control (SPC)
The field of Statistical Process Control (SPC) is closely-related to univariate
outlier detection methods. It considers the case where the univariable stream
of measures represents a stochastic process, and the detection of the outlier is
required online. SPC methods are being applied for more than half a century
and were extensively investigated in statistics literature.
Ben-Gal et al. (Gal et al., 2003) categorize SPC methods by two major
criteria: i) methods for independent data versus methods for dependent data;
and ii) methods that are model-specific, versus methods that are model-generic.
Model specific methods require a-priori assumptions on the process characteristics,
usually defined by an underlying analytical distribution or a closed-form
expression. Model-generic methods try to estimate the underlying model with
minimum a-priori assumptions.
Traditional SPC methods, such as Shewhart, Cumulative Sum (CUSUM)
and Exponential Weighted Moving Average (EWMA) are model-specific for
independent data. Note that these methods are extensively implemented in industry,
although the independence assumptions are frequently violated in prac-
tice.
The majority of model-specific methods for dependent data are based on
time-series. Often, the underlying principle of these methods is as follows: find
a time series model that can best capture the autocorrelation process, use this
model to filter the data, and then apply traditional SPC schemes to the stream
of residuals. In particular, the ARIMA (Auto Regressive Integrated Moving
Average) family of models is widely implemented for the estimation and filtering
of process autocorrelation. Under certain assumptions, the residuals of
the ARIMA model are independent and approximately normally distributed, to
which traditional SPC can be applied. Furthermore, it is commonly conceived
that ARIMA models, mostly the simple ones such as AR(see Equation 1.1),
can effectively describe a wide variety of industry processes (Box, 1976; Apley
and Shi, 1999).
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
Outlier Detection 7
Model-specific methods for dependent data can be further partitioned to
parameter-dependent methods that require explicit estimation of the model
parameters (e.g., (Alwan and Roberts, 1988; Wardell et al., 1994; Lu and
Reynolds, 1999; Runger and Willemain, 1995; Apley and Shi, 1999)), and
to parameter-free methods, where the model parameters are only implicitly
derived, if at all (Montgomery and Mastrangelo, 1991; Zhang, 1998).
The Information Theoretic Process Control (ITPC) is an example for a modelgeneric
SPC method for independent data, proposed in (Alwan et al., 1998).
Finally, a model-generic SPC method for dependent data is proposed in (Gal
et al., 2003).
4. Multivariate Outlier Detection
In many cases multivariable observations can not be detected as outliers
when each variable is considered independently. Outlier detection is possible
only when multivariate analysis is performed, and the interactions among
different variables are compared within the class of data. A simple example
can be seen in Figure 1.1, which presents data points having two measures on
a two-dimensional space. The lower left observation is clearly a multivariate
outlier but not a univariate one. When considering each measure separately
with respect to the spread of values along the x and y axes, we can is seen
that they fall close to the center of the univariate distributions. Thus, the test
for outliers must take into account the relationships between the two variables,
which in this case appear abnormal.
Figure 1.1. A Two-Dimensional Space with one Outlying Observation (Lower Left Corner).
Data sets with multiple outliers or clusters of outliers are subject to masking
and swamping effects. Although not mathematically rigorous, the following
definitions from (Acuna and Rodriguez, 2004) give an intuitive understandBen-Gal
I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
8
ing for these effects (for other definitions see (Hawkins, 1980; Iglewics and
Martinez, 1982; Davies and Gather, 1993; Barnett and Lewis, 1994)):
Masking effect It is said that one outlier masks a second outlier, if the second
outlier can be considered as an outlier only by itself, but not in the presence
of the first outlier. Thus, after the deletion of the first outlier the
second instance is emerged as an outlier. Masking occurs when a cluster
of outlying observations skews the mean and the covariance estimates
toward it, and the resulting distance of the outlying point from the mean
is small.
Swamping effect It is said that one outlier swamps a second observation, if
the latter can be considered as an outlier only under the presence of the
first one. In other words, after the deletion of the first outlier the second
observation becomes a non-outlying observation. Swamping occurs
when a group of outlying instances skews the mean and the covariance
estimates toward it and away from other non-outlying instances, and the
resulting distance from these instances to the mean is large, making them
look like outliers. A single step procedure with low masking and swamping
is given in (Iglewics and Martinez, 1982).
4.1 Statistical Methods for Multivariate Outlier Detection
Multivariate outlier detection procedures can be divided to statistical methods
that are based on estimated distribution parameters, and data-mining related
methods that are typically parameter-free.
Statistical methods for multivariate outlier detection often indicate those
observations that are located relatively far from the center of the data distribution.
Several distance measures can be implemented for such a task. The
Mahalanobis distance is a well-known criterion which depends on estimated
parameters of the multivariate distribution. Given n observations from a pdimensional
dataset (oftenn¿¿ p), denote the sample mean vector by ¯xn and
the sample covariance matrix by Vn , where
Vn =
1
n − 1
n
i=1
(xi − ¯xn) (xi − ¯xn)T
(1.3)
The Mahalanobis distance for each multivariate data point i, i = 1, . . . , n,
is denoted by Mi and given by
Mi =
n
i=1
(xi − ¯xn)T
V−1
n (xi − ¯xn)
1/2
. (1.4)
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
Outlier Detection 9
Accordingly, those observations with a large Mahalanobis distance are indicated
as outliers. Note that masking and swamping effects play an important
rule in the adequacy of the Mahalanobis distance as a criterion for outlier detection.
Namely, masking effects might decrease the Mahalanobis distance of
an outlier. This might happen, for example, when a small cluster of outliers
attracts ¯xn and inflate Vn towards its direction. On the other hand, swamping
effects might increase the Mahalanobis distance of non-outlying observations.
For example, when a small cluster of outliers attracts ¯xn and inflate Vn away
from the pattern of the majority of the observations (see (Penny and Jolliffe,
2001)).
4.2 Multivariate Robust Measures
As in one-dimensional procedures, the distribution mean (measuring the location)
and the variance-covariance (measuring the shape) are the two most
commonly used statistics for data analysis in the presence of outliers
(Rousseeuw and Leory, 1987). The use of robust estimates of the multidimensional
distribution parameters can often improve the performance of the
detection procedures in presence of outliers. Hadi (Hadi, 1992) addresses this
problem and proposes to replace the mean vector by a vector of variable medians
and to compute the covariance matrix for the subset of those observations
with the smallest Mahalanobis distance. A modified version of Hadi’s procedure
is presented in (Penny and Jolliffe, 2001). Caussinus and Roiz (Caussinus
and Roiz, 1990) propose a robust estimate for the covariance matrix, which is
based on weighted observations according to their distance from the center.
The authors also propose a method for a low dimensional projections of the
dataset. They use the Generalized Principle Component Analysis (GPCA) to
reveal those dimensions which display outliers. Other robust estimators of the
location (centroid) and the shape (covariance matrix) include the minimum
covariance determinant (MCD) and the minimum volume ellipsoid (MVE)
(Rousseeuw, 1985; Rousseeuw and Leory, 1987; Acuna and Rodriguez, 2004).
4.3 Data-Mining Methods for Outlier Detection
In contrast to the above-mentioned statistical methods, data-mining related
methods are often non-parametric, thus, do not assume an underlying generating
model for the data. These methods are designed to manage large databases
from high-dimensional spaces. We follow with a short discussion on three related
classes in this category: distance-based methods, clustering methods and
spatial methods.
Distance-based methods were originally proposed by Knorr and Ng (Knorr
and Ng, 1997; Knorr and Ng, 1998). An observation is defined as a distancebased
outlier if at least a fraction β of the observations in the dataset are further
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
10
than r from it. Such a definition is based on a single, global criterion determined
by the parameters r and β. As pointed out in Acuna and Rodriguez
(2004), such definition raises certain difficulties, such as the determination of
r and the lack of a ranking for the outliers. The time complexity of the algorithm
is O(pn2), where p is the number of features and n is the sample size.
Hence, it is not an adequate definition to use with very large datasets. Moreover,
this definition can lead to problems when the data set has both dense and
sparse regions (Breunig et al., 2000; Ramaswamy et al., 2000; Papadimitriou
et al., 2002). Alternatively, Ramaswamy et al. (Ramaswamy et al., 2000) suggest
the following definition: given two integers v and l (v¡ l), outliers are
defined to be the top l sorted observations having the largest distance to their
v-th nearest neighbor. One shortcoming of this definition is that it only considers
the distance to the v-th neighbor and ignores information about closer
observations. An alternative is to define outliers as those observations having
a large average distance to the v-th nearest neighbors. The drawback of this
alternative is that it takes longer to be calculated (Acuna and Rodriguez, 2004).
Clustering based methods consider a cluster of small sizes, including the
size of one observation, as clustered outliers. Some examples for such methods
are the partitioning around medoids (PAM) and the clustering large applications
(CLARA) (Kaufman and Rousseeuw, 1990); a modified version of the
latter for spatial outliers called CLARANS (Ng and Han, 1994); and a fractaldimension
based method (Barbara and Chen, 2000). Note that since their main
objective is clustering, these methods are not always optimized for outlier detection.
In most cases, the outlier detection criteria are implicit and cannot
easily be inferred from the clustering procedures (Papadimitriou et al., 2002).
Spatial methods are closely related to clustering methods. Lu et al. (Lu
et al., 2003) define a spatial outlier as a spatially referenced object whose
non-spatial attribute values are significantly different from the values of its
neighborhood. The authors indicate that the methods of spatial statistics can
be generally classified into two sub categories: quantitative tests and graphic
approaches. Quantitative methods provide tests to distinguish spatial outliers
from the remainder of data. Two representative approaches in this category
are the Scatterplot (Haining, 1993; Luc, 1994) and the Moran scatterplot (Luc,
1995). Graphic methods are based on visualization of spatial data which highlights
spatial outliers. Variogram clouds and pocket plots are two examples for
these methods (Haslett et al., 1991; Panatier, 1996). Schiffman et al. (Schiffman
et al., 1981) suggest using a multidimensional scaling (MDS) that represents
the similarities between objects spatially, as in a map. MDS seeks
to find the best configuration of the observations in a low dimensional space.
Both metric and non-metric forms of MDS are proposed in (Penny and Jolliffe,
2001). As indicated above, Ng and Han (Ng and Han, 1994) develop a
clustering method for spatial data-mining called CLARANS which is based on
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
Outlier Detection 11
randomized search. The authors suggest two spatial data-mining algorithms
that use CLARANS. Shekhar et al. (Shekhar and Lu, 2001; Shekhar and Lu,
2002) introduce a method for detecting spatial outliers in graph data set. The
method is based on the distribution property of the difference between an attribute
value and the average attribute value of its neighbors. Shekhar et al.,
(Shekhar and Lu, 2003) propose a unified approach to evaluate spatial outlierdetection
methods. Lu et al. (Lu et al., 2003) propose a suite of spatial outlier
detection algorithms to minimize false detection of spatial outliers when their
neighborhood contains true spatial outliers.
Applications of spatial outliers can be found in fields where spatial information
plays an important role, such as, ecology, geographic information systems,
transportation, climatology, location-based services, public health and public
safety (Ng and Han, 1994; Shekhar and Chawla, 2002; Lu et al., 2003).
4.4 Preprocessing Procedures
Different paradigms were suggested to improve the efficiency of various
data analysis tasks including outlier detection. One possibility is to reduce the
size of the data set by assigning the variables to several representing groups.
Another option is to eliminate some variables from the analyses by methods of
data reduction (Barbara et al., 1996), such as methods of principal components
and factor analysis that are further discussed in Chapter XXXX of this volume.
Another means to improve the accuracy and the computational tractability
of multiple outlier detection methods is the use of biased sampling. Kollios et
al. (Kollios et al., 2003) investigate the use of biased sampling according to the
density of the data set to speed up the operation of general data-mining tasks,
such as clustering and outlier detection.
5. Comparison of Outlier Detection Methods
Since different outlier detection algorithms are based on disjoints sets of
assumption, a direct comparison between them is not always possible. In many
cases, the data structure and the outlier generating mechanism on which the
study is based dictate which method will outperform the others. There are few
works that compare different classes of outlier detection methods.
Williams et al. (Williams et al., 2002), for example, suggest an outlier detection
method based on replicator neural networks (RNNs). They provide a
comparative study of RNNs with respect to two parametric (statistical) methods
(one proposed in (Hadi, 1994), and the other proposed in (Knorr et al.,
2001)) and one data-mining non-parametric method (proposed in (Oliver et al.,
1996)). The authors find that RNNs perform adequately to the other methods
in many cases, and particularly well on large datasets. Moreover, they find that
some statistical outlier detection methods scale well for large dataset, despite
Ben-Gal I., Outlier detection, In: Maimon O. and Rockach L. (Eds.) Data Mining and
Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers," Kluwer Academic Publishers, 2005, ISBN 0-387-24435-2.
12
claims to the contrary in the data-mining literature. They summaries the study
by pointing out that in outlier detection problems simple performance criteria
do not easily apply.
Shekhar et al. (Shekhar and Lu, 2003) characterize the computation structure
of spatial outlier detection methods and present scalable algorithms to
which they also provide a cost model. The authors present some experimental
evaluations of their algorithms using a traffic dataset. Their experimental results
show that the connectivity-clustered access model (CCAM) achieves the
highest clustering efficiency value with respect to a predefined performance
measure. Lu et al. (Lu et al., 2003) compare three spatial outlier detection
algorithms. Two algorithms are sequential and one algorithm based on median
as a robust measure for the mean. Their experimental results confirm the effectiveness
of these approaches in reducing the risk of falsely negative outliers.
Finally, Penny and Jolliffe (Penny and Jolliffe, 2001) conduct a comparison
study with six multivariate outlier detection methods. The methods’ properties
are investigated by means of a simulation study and the results indicate that
no technique is superior to all others. The authors indicate several factors that
affect the efficiency of the analyzed methods. In particular, the methods depend
on: whether or not the data set is multivariate normal; the dimension of
the data set; the type of the outliers; the proportion of outliers in the dataset;
and the outliers’ degree of contamination (outlyingness). The study motivated
the authors to recommend the use of a ”battery of multivariate methods” on
the dataset in order to detect possible outliers. We fully adopt such a recommendation
and argue that the battery of methods should depend, besides on
the above-mentioned factors, but also on other factors such as, the data structure
dimension and size; the time constraints in regard to single vs. sequential
identifiers; and whether an online or an offline outlier detection is required.
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