SURVEY PAPER ON OUT LIER DETECTION USING FUZZY LOGIC BASED METHOD

International Journal on Cybernetics & Informatics (IJCI) Vol. 6, No. 1/2, April 2017
DOI: 10.5121/ijci.2017.6204 29
SURVEY PAPER ON OUT LIER DETECTION USING
FUZZY LOGIC BASED METHOD
Deepa Verma, Rakesh Kumar and Akhilesh Kumar
Department of Information Technology,
Rajkiya Engineering College, Ambedkar Nagar (U.P) – 224 122, India
ABSTRACT
Fuzzy logic can be used to reason like humans and can deal with uncertainty other than randomness.
Outlier detection is a difficult task to be performed, due to uncertainty involved in it. The outlier itself is a
fuzzy concept and difficult to determine in a deterministic way. fuzzy logic system is very promising, since
they exactly tackle the situation associated with outliers. Fuzzy logic that addresses the seemingly
conflicting goals (i) removing noise, (ii) smoothing out outliers and certain other salient feature. This
paper provides a detailed fuzzy logic used for outlier detection by discussing their pros and cons. Thus this
is a very helpful document for naive researchers in this field.
KEYWORDS
Data mining, fuzzy logic, Outlier Detection. Artificial Intelligent Information Systems
1. INTRODUCTION
In a sequence of measurements or generic investigational data the outlier is defined as an
observation that deviates too much from other observations and it is implicit to be generated by a
mechanism different from other observations. The inlier, on the other hand, is defined as an
observation that is explained by underlying probability density function. The exact definition of
an outlier depends on the context [1]. Several definitions have been given and can be roughly
grouped into six categories [2]: I) distribution based, II) depth-based, III) distance-based, IV)
clustering based, V) density-based VI) categorical-based. As a low local density of other data on
the observation is an indication of a possible outlier, all the above listed definitions substantially
come from different strategies for density estimation. The presence of the outliers is not always a
drawback. For instance, in clustering applications, outliers are considered as noise observations
that should be removed in order to perform a more reliable clustering, while in data mining
applications, the detection of anomalous patterns in data is more interesting than the detection of
inliers. In some applications in fault diagnosis, the outliers can be the indicators of a faulty in the
monitored system, thus their presence must be carefully pointed out. When the experimental data
are used to validate a model and/or optimize some of its internal parameters, the presence of
outliers should be avoided and, for this reason, usually a preprocessing of the data is mandatory in
order to filter out such anomalous observation. In the steelmaking field a large amount of data
are collected concerning the chemical composition of a steel cast and its derived products at the
different stages of the manufacture as well as process variables, measurements and analyses
concerning the properties of a each particular manufactured product. While process variables are
normally extracted in an automatic way from the plant information system, some of the results of
the chemical analyses, mechanical tests and inspections are still manually input by the technical
personnel. Errors are thus possible on the values that are recorded by hand, some of which are
quite easily recognized (for instance typographic errors) but most of them are not so easy to point
out. Moreover, anomalous events or faults in the measuring devices as well as in the machinery

30
itself can results in measurements that are indeed anomalous but do not differ in the order of
magnitude with respect to standard data. Thus their detection cannot be performed via trivial tests
and/or visual inspection, but rather through the implementation of suitable detection strategies,
capable of pointing out anomalous data in an efficient and rapid way. Efficiency and speed are
often conflicting requirements especially when treating big amount of data, as actually in the case
of steelmaking industry us. Fuzzy logic is an approach to computing based on "degrees of truth"
rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is
based. Fuzzy logic is capable of supporting human type of reasoning in natural form to a
considerable extent. It does so by allowing partial membership for data items in fuzzy subsets.
Integration of fuzzy logic with data mining techniques has become one of the key aspects of soft
computing in handling the challenges posed by the massive collection of natural data. The fuzzy
set is different from a crisp set in that it allows the elements to have a degree of membership. The
core of a fuzzy set is its membership function which defines the relationship between a value in
the set’s domain and its degree of membership in the fuzzy set. The relationship is functional
because it returns a single degree of membership for any value in the domain.
2. LITERATURE SURVEY
Crisp set can be used to handle structured data which is generally free from outlier points. But it
is difficult to handle formless natural data which often contain outlier data points. Fuzzy set
(introduced by Zadeh [11]) is different from its crisp counterpart, as it allows the elements to have
a level of membership. Thus, to handle unstructured natural data which is qualitative and
imprecise in nature, many of the data mining techniques are integrated with fuzzy logic [12]. In
traditional clustering approaches each pattern belongs to one and only one cluster [13]. In case of
fuzzy clustering, the membership function associates each pattern with all the clusters. Thus fuzzy
clusters grow in their natural shapes. In this section some fuzzy clustering outlier detection
techniques are analyzed.
Fuzzy C-Means algorithm (FCM) [14]: FCM is one of the well-known fuzzy clustering
algorithms, and used in a wide variety of applications, such as medical imaging, remote sensing,
data mining and pattern recognition [15,16,17,18,19]. Chiu, Yager & Filev have proposed Fuzzy
c-means data clustering algorithm in which each data point belongs to a clustering to a degree
specified by a membership grade [20]. Fuzzy c-means clustering(FCM) has two processes: the
calculation of cluster centers and the assignment of points to these centers using a form of
Euclidian distance. This process is repeated until the cluster centers are stabilized. FCM partitions
a collection of data points into fuzzy groups. Unlike k-means where data point must exclusively
belong to one cluster center, FCM finds a cluster center in each group in order to minimize the
objective function of the dissimilarity measure. FCM employs fuzzy partitioning so that a given
data point can belong to several groups with the degree of belongingness specified by
membership grades between 0 and 1. The membership of each data point corresponding to each
cluster center will be calculated on the basis of distance between the cluster center and the data
point.
Multiple Outlier Detection in Multivariate Data Using Self-Organizing Maps Title [27] Nag
A.K., Mitra A. and Mitra S. proposed an artificial intelligence technique of self-organizing map
(SOM) based non-parametric method for outlier detection. It can be used to detect outliers from
large multidimensional datasets and also provides information about the entire outlier
neighborhood. SOM is a feed forward neural network that uses an unsupervised training
algorithm, and through a process called self-organization, configures the output units into a
topological representation of the original data (Kohonen 1997). SOM produces a topology-
preserving mapping of the multidimensional data cloud onto lower dimensional visualizable plane

31
and hence provides an easy way of detection of multidimensional outliers in the data, at
respective levels of influence.
3. DIFFERENT ASPECTS OF AN OUTLIER DETECTION PROBLEM
In this section discusses the different aspects of outlier detection. As mentioned earlier, a specific
formulation of the problem is determined by several different factors such as the input data,
unsupervised data, the availability (or unavailability) of other resources as well as the constraints
and requirements induced by the application domain. This section brings forth the richness in the
problem domain and motivates the need for so many diverse techniques.
(i) input data: a key component of any outlier detection technique is the input data in which it has
to detect the outliers. Input is generally treated as a collection of data objects or data instances
(also referred to as record, point, vector, pattern, event, case, sample, observation, or entity) [tan
et al. 2005a]. Each data instance can be described using a set of attributes (also referred to as
variable, characteristic, feature, field, or dimension). The data instances can be of different types
such as binary, categorical or continuous. Each data instance might consist of only one attribute
(univariate) or multiple attributes (multivariate). In the case of multivariate data instances, all
attributes might be of same type or might be a mixture of different data types.
(ii)Type of Supervision: Besides the input data (or observations), an outlier detection algorithm
might also have some additional information at its disposal. A labeled training data set is one such
information which has been used extensively (primarily by outlier detection techniques based on
concepts from machine learning [Mitchell 1997] and statistical learning theory [Vapnik 1995]). A
training data set is required by techniques which involve building an explicit predictive model.
The labels associated with a data instance denote if that instance is normal or outlier1. Based on
the extent to which these labels are utilized, outlier detection techniques can be divided into three
categories
(ii)a. Supervised outlier detection techniques. First category techniques assume the availability of
a training data set which has labeled instances for normal as well as outlier class. Typical
approach in such case is to build predictive models for both normal and outlier classes. Any
unseen data instance is compared against the two models to determine which class it belongs to.
Supervised outlier detection techniques have an explicit notion of the normal and outlier behavior
and hence accurate models can be built. One drawback here is that accurately labeled training
data might be prohibitively expensive to obtain. Labeling is often done manually by a human
expert and hence requires a lot of effort to obtain the labeled training data set. Certain techniques
inject artificial outliers in a normal data set to obtain a fully labeled training data set and then
apply supervised outlier detection techniques to detect outliers in test data [Abe et al. 2006].
(ii)b. Semi-Supervised outlier detection techniques. Second category of techniques assume the
availability of labeled instances for only one class. it is often difficult to collect labels for other
class. For example, in space craft fault detection, an outlier scenario would signify an accident,
which is not easy to model. The typical approach of such techniques is to model only the
available class and decare any test instance which does not fit this model to belong to the other
class.
(ii)c. Unsupervised outlier detection techniques. The third category of techniques do not make
any assumption about the availability of labeled training data. Thus these techniques are most
widely applicable. The techniques in this category make other assumptions about the data. For
example, parametric statistical techniques, assume a parametric distribution of one or both classes

32
of instances. Similarly, several techniques make the basic assumption that normal instances are
far more frequent than outliers. Thus a frequently occurring pattern is typically considered normal
while a rare occurrence is an outlier. The unsupervised techniques typically suffer from higher
false alarm rate, because often times the underlying assumptions do not hold true.
4. OUTLIER DETECTION METHODS IN INDUSTRIAL SENSOR DATA
The occurrence of outliers in industrial data is handle rank deficiency. Comparative studies of
these methods is often the rule rather than the exception. Many standard outlier detection methods
fail to detect outliers in industrial data because of the high data dimensionality. These problems
can he solved by using robust model-based methods that do not require the data to be of full rank.
Jordaan and Smits [5] propose a robust model-based outlier detection approach that exploits the
characteristics of the support vectors extracted by the Support Vector Machine method [6]. The
method makes use of several models of varying complexity to detect outliers based on the
characteristics of the support vectors obtained from SVM-models. This has the advantage that the
decision does not depend on the quality of a single model, which adds to the robustness of the
approach. Furthermore, since it is an iterative approach, the most severe outliers are removed
first. This allows the models in the next iteration to learn from cleaner” data and thus reveal
outliers that were masked in the initial model. The need for several iterations as well as the use of
models, however, makes the on-line application of this method difficult for the not negligible
computational burden. Moreover, if the data to be on-line processes come from dynamic systems,
that tend to change their conditions through time, although not rapidly, the models update is
required and this can furtherly slow the outlier detection. On the other hand, when low
computational burden is required due to strict time constraints, there is a quite wide variety of fast
methods. One of the most commonly used is the Grubbs test [7], a standard and widely applied
statistical procedure, which however, to be appropriately applied, requires that data distribution
can be reasonably approximated by a normal distribution, as it takes into account only the mean
and standard deviation of the data distribution. Also in [8] a fast outlier detection method is
proposed which uses the local correlation integral (LOCI). This method is highly effective for
detecting outliers and groups of outliers, although it takes into consideration only the number of
neighboring points. Neural Networks have been successfully applied for outliers detection, often
coupled with techniques such as Principal Component Analysis (PCA) [9] and Partial Least
Squares (PLS) [10]: for instance in [11] outliers are found by investigating points at the edges of
previously constructed clusters. The neural networks used here have class coded output nodes.
Outliers should not activate any output node. If the maximal output value of the neural network is
less than a rejection threshold, then the input pattern is rejected.
5. APPLICATIONS OF OUTLIER DETECTION
The ability to detect outliers is a highly desirable feature in application domains for a variety of
reasons. In this section we discuss some of the popular applications of outlier detection. For each
application domain we discuss the significance of outliers, the challenges unique to that domain
and the popular approaches for outlier detection that have been adopted in that domain.
(i)Intrusion Detection: Intrusion detection refers to detection of malicious activity (break-ins,
penetrations, and other forms of computer abuse) in a computer related system [Phoha 2002].
These malicious activities or intrusions are very interesting from a computer security perspective.
An intrusion is different from the normal behavior of the system. This property allows a direct
formulation of the problem as an outlier detection problem. Outlier detection techniques have
been widely applied for intrusion detections.

33
(ii) Fraud Detection: Fraud detection refers to detection of criminal activities occurring in
commercial organizations such as banks, credit card companies, insurance agencies, cell phone
companies, stock market etc. The malicious users might be the actual customers. of the
organization or might be posing as a customer (also known as identity theft). The fraud occurs
when these users consume the resources provided by the organization in an unauthorized way.
The organizations are interested in immediate detection of such frauds to prevent economic
losses.
6. ADVANTAGES AND DISADVANTAGES OF FUZZY LOGIC BASED METHOD
Advantages:
1. The use of a fuzzy set approach to pattern classification inherently provides degree of
membership information that is extremely useful in higher level decision making.
2. Fuzzy Logic allows you to model in a more intuitive way complex dynamic systems.
3. Fuzzy logic is capable of at the bottom of, human type reasoning in natural form by
allowing partial membership for data items in fuzzy subsets [29].
4. Fuzzy logic can handle uncertainty at various stages.
5. With this approach knowledge can be expressed in terms of If–THEN rules.
Disadvantage:
Disadvantage of fuzzy clustering is that with a growing number of objects the amount of output
of the results becomes huge, so that the information received often cannot be worked.
7. CONCLUSION AND FUTURE SCOPE
In this survey we have discussed different ways in which the problem of outlier detection has
been formulated in literature. outlier detection algorithms are available which are based on fuzzy
logic and have their own advantages and disadvantages. Some researchers have used hybrid
approach, based on fuzzy logic and neural network to combine the advantages and to overcome
the disadvantages of both the techniques for various domain. But very few have used this hybrid
approach for outlier detection. Thus, there is still a wide scope to apply -neural techniques in this
area to improve the quality and required time of outlier detection process. In this paper we
discussed the techniques and methods used by different researchers and basic introduction and
algorithms are discussed in brief. At the end, we conclude that the outlier detection is must to
increase the speed of processing of data and also helpful in reduction of processing cost of data.
We have to compare all the techniques which are effectively used till the time and get the best
one as the result of this comparison and reliable to implement. Try to achieve the objectives
which are defined previously.
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AUTHORS
Akhilesh Kumar graduated from Mahatma Ghandhi Mission’s college of Engg. and
technology, Noida, Uttar Pradesh in Computer Science & Engineering in 2010. He has
been M.Tech in the department of Computer Science & Engineering, Kamla Nehru
Institute of Technology, Sultanpur (Uttar Pradesh). SinceAugust2012, he has been with the
Department of Department of Information Technology, Rajkiya EngineeringCollege,
Ambedkar Nagar, as an Assistant Professor.His area of interests includes Computer
Networks and Mobile ad-hoc Nerwork.
Rakesh Kumar was born in Bulandshahr (U.P.), India, in 1984. He received the B.Tech.
degree in Information Technology from Kamla Nehru Institute of Technology, Sultanpur
(U.P.), India, in 2007, and the M.Tech. degrees in ICT Specialization with Software
Engineering from the Gautam Buddha University, Greater Noida, Gautam Budh Nagar,
Uttar Pradesh, India, in 2012.In 2007, he joined the Quantum Technology, New Delhi as a
Software Engineer and Since August 2012, he has been with the Department of
Department of Information Technology, Rajkiya Engineering College, Ambedkar Nagar, as an Assistant
Professor. His current research interests include Computer Network, Multicast Security, Sensor Network
and data mining. He is a Life Member of the Indian Society for Technical Education (ISTE), and he is a
Nominee Member of Computer society of India.

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