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Data Mining Techniques for Outlier Detection

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DOI: 10.4018/978-1-4666-2455-9.ch009
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MLA

Suri, N, M Murty and G Athithan. "Data Mining Techniques for Outlier Detection." Data Mining: Concepts, Methodologies, Tools, and Applications. IGI Global, 2013. 159-178. Web. 25 Oct. 2014. doi:10.4018/978-1-4666-2455-9.ch009

APA

Suri, N., Murty, M., & Athithan, G. (2013). Data Mining Techniques for Outlier Detection. In Data Mining: Concepts, Methodologies, Tools, and Applications (pp. 159-178). Hershey, PA: Information Science Reference. doi:10.4018/978-1-4666-2455-9.ch009

Chicago

Suri, N, M Murty and G Athithan. "Data Mining Techniques for Outlier Detection." In Data Mining: Concepts, Methodologies, Tools, and Applications, 159-178 (2013), accessed October 25, 2014. doi:10.4018/978-1-4666-2455-9.ch009

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Abstract

Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Detecting the objects in a data set with unusual properties is important as such outlier objects often contain useful information on abnormal behavior of the system described by the data set. Outlier detection has been popularly used for detection of anomalies in computer networks, fraud detection and such applications. Though a number of research efforts address the problem of detecting outliers in data sets, there are still many challenges faced by the research community in terms of identifying a suitable technique for addressing specific applications of interest. These challenges are primarily due to the large volume of high dimensional data associated with most data mining applications and also due to the performance requirements. This chapter highlights some of the important research issues that determine the nature of the outlier detection algorithm required for a typical data mining application. The research issues discussed include the method of outlier detection, size and dimensionality of the data set, and nature of the target application. Thus this chapter attempts to cover the challenges and possible research directions along with a survey of various data mining techniques dealing with the outlier detection problem.
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Introduction

The recent developments in the field of data mining have lead to the outlier detection process mature as one of the popular data mining tasks. Due to its significance in the data mining process, outlier detection is also known as outlier mining. Typically, outliers are data objects that are significantly different from the rest of the data. Outlier detection or outlier mining refers to the process of identifying such rare objects in a given data set. Although rare objects are known to be fewer in number, their significance is high compared to other objects, making their detection an important task. The general requirement of this task is to identify and remove the contaminating effect of the outlying objects on the data and as such to purify the data for further processing. More formally, the outlier detection problem can be defined as follows: given a set of data objects, find a specific number of objects that are considerably dissimilar, exceptional and inconsistent with respect to the remaining data (Han, 2000). A number of new techniques have been proposed recently in the field of data mining to solve this problem. This chapter mainly deals with these techniques for outlier detection and highlights their relative merits and demerits.

Many data mining algorithms try to minimize the influence of outliers or eliminate them all together. However, this could result in the loss of important hidden information since one person’s noise could be another person’s signal (Knorr, 2000). Thus, the outliers themselves may be of particular interest, as in the case of fraud detection, where they may indicate some fraudulent activity. Besides fraud detection, financial applications and niche marketing and network intrusion detection are other applications of outlier detection, making it an interesting and important data-mining task. Depending on the application domain, outlier detection has been variously referred to as novelty detection (Markou, 2003a), chance discovery (McBurney, 2003), or exception mining (Suzuki, 2000), etc. A related field of research is activity monitoring with the purpose of detecting illegal access. This task consists of monitoring an online data source in the search for unusual behavior (Fawcett, 1999).

Much of the research related to outlier detection has evolved in the context of anomaly detection. An anomaly is something that is different from normal behavior. Though anomalies are often considered as noise, they could be deemed as the early indicators of a possible major adverse effect. Thus, detection of anomalies is important in its own right and also due to the increasing number of applications like computer network intrusion detection (Chandola, 2009; Lazarevic, 2003), fraud detection, astronomical data analysis (Chaudhary, 2002), etc. In most of the cases, anomaly detection is intended to understand evolving new phenomena that is not seen in the past data. A standard method for detecting anomalies is to create a model of the normal data and compare the future observations against the model. However, as the definition of normality differs across various problem domains, the problem of anomaly detection turns out to be a more challenging and involved process. A generic computational approach is to look for outliers in the given data set. Some research efforts in this direction can be found in (Lazarevic, 2003; Sithirasenan, 2008), in the context of network intrusion detection.

There have been various definitions in the literature for outliers that were proposed in different research contexts. A popular one among them, given by (Hawkins, 1980), is to define an outlier as an observation that deviates so much from other observations as to arouse suspicion that it is generated by a different mechanism. The presence of an outlying object in a data set shows itself in some form or the other. For example, data objects P and Q in Figure 1(a) are outliers, which is obvious from a visual examination. However, in cases where the objects like P1 and P2 in Figure 1(b) are present in a data set, identifying them as outliers requires some extra effort. Also, depending on the relative position and grouping of outliers in a data set, some specialized techniques are required for their detection.

Figure 1.

Sample outliers depicted in two example scenarios

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Complete Chapter List

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Table of Contents
Chapter 1
Sangeetha Kutty, Richi Nayak, Tien Tran
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Chapter 2
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Finding Persistent Strong Rules: Using Classification to Improve Association Mining
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Chapter 3
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Chapter 4
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Chapter 7
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Chapter 8
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Online Clustering and Outlier Detection
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Chapter 9
N Suri, M Murty, G Athithan
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Data Mining Techniques for Outlier Detection
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Chapter 10
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Chapter 14
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Heterogeneous Text and Numerical Data Mining with Possible Applications in Business and Financial Sectors
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Chapter 44
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Chapter 46
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Chapter 49
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Chapter 52
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Chapter 55
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Chapter 56
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Chapter 57
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Chapter 59
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Chapter 60
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Chapter 61
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Chapter 73
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Chapter 77
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Chapter 79
Asem Omari
Selling products or services online plays an important role in the success of businesses that have a physical presence, like a retail business. For... Sample PDF
Supporting Companies Management and Improving their Productivity through Mining Customers Transactions
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Chapter 80
M. Govindarajan, RM. Chandrasekaran
Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often... Sample PDF
A Hybrid Multilayer Perceptron Neural Network for Direct Marketing
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Chapter 81
Ori Gudes, Elizabeth Kendall, Tan Yigitcanlar, Jung Hoon Han, Virendra Pathak
This chapter investigates the challenges and opportunities associated with planning for a competitive city. The chapter is based on the assumption... Sample PDF
Developing a Competitive City through Healthy Decision-Making
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Chapter 82
Nermin Ozgulbas, Ali Serhan Koyuncugil
Risk management has become a vital topic for all enterprises especially in financial crisis periods. All enterprises need systems to warn against... Sample PDF
Financial Early Warning System for Risk Detection and Prevention from Financial Crisis
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Chapter 83
Ibrahiem Mahmoud Mohamed El Emary
This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of... Sample PDF
Role of Data Mining and Knowledge Discovery in Managing Telecommunication Systems
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Chapter 84
Rubén Jaramillo Vacio, Carlos Alberto Ochoa Ortiz Zezzatti, Armando Rios
This chapter describes the experimental study partial discharges (PD) activities with artificial intelligent tools. The results present different... Sample PDF
Data Mining Applications in the Electrical Industry
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Chapter 85
Luciana Romani, Elaine de Sousa, Marcela Ribeiro, Ana de Ávila, Jurandir Zullo, Caetano Traina, Agma Traina
This chapter discusses how to take advantage of computational models to analyze and extract useful information from time series of climate data and... Sample PDF
Mining Climate and Remote Sensing Time Series to Improve Monitoring of Sugar Cane Fields
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Chapter 86
Anass El Haddadi, Bernard Dousset, Ilham Berrada
Competitive intelligence activities rely on collecting and analyzing data in order to discover patterns from data using sequence data mining. The... Sample PDF
Discovering Patterns in Order to Detect Weak Signals and Define New Strategies
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Chapter 87
Mieke Jans, Nadine Lybaert, Koen Vanhoof
Economic crime is a billion dollar business and is substantially present in our current society. Both researchers and practitioners have gone into... Sample PDF
Data Mining and Economic Crime Risk Management
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Chapter 88
Sociocognitive Inquiry  (pages 1688-1708)
Brian R. Gaines, Mildred L. G. Shaw
This chapter describes techniques for sociocognitive inquiry based on conceptual grid elicitation and analysis using web-based tools, such as... Sample PDF
Sociocognitive Inquiry
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Chapter 89
Yassine Lassoued, Trung T. Pham, Luis Bermudez, Karen Stocks, Eoin O’Grady, Anthony Isenor, Paul Alexander
This chapter defines the coastal web atlases interoperability problem, introduces interoperability standards, and describes the development of a... Sample PDF
Coastal Atlas Interoperability
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Chapter 90
D. A. Nembhard, K. K. Yip, C. A. Stifter
Developmental psychology is the scientific study of progressive psychological changes that occur in human beings as they age. Some of the current... Sample PDF
Association Rule Mining in Developmental Psychology
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Chapter 91
Jawahitha Sarabdeen
E-healthcare systems through electronic medical records (EMRs), electronic prescribing and decision support systems endeavour to reduce the cost and... Sample PDF
Legal Issues in E-Healthcare Systems
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Chapter 92
Diane A. Desierto
This paper describes Bayan Muna et al. v. Mendoza et al., a 2009 Philippine Supreme Court petition involving the first and ongoing certiorari... Sample PDF
Privacy Expectations in Passive RFID Tagging of Motor Vehicles: Bayan Muna et al. v. Mendoza et al. in the Philippine Supreme Court
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Chapter 93
William H. Horsthemke, Daniela S. Raicu, Jacob D. Furst, Samuel G. Armato
Evaluating the success of computer-aided decision support systems depends upon a reliable reference standard, a ground truth. The ideal gold... Sample PDF
Evaluation Challenges for Computer-Aided Diagnostic Characterization: Shape Disagreements in the Lung Image Database Consortium Pulmonary Nodule Dataset
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Chapter 94
Alan Olinsky, Phyllis A. Schumacher, John Quinn
One way to enhance the likelihood that more students will graduate within the specific major that they begin with is to attract the type of students... Sample PDF
Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate
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Chapter 95
Manish Gupta, Jiawei Han
In this chapter we first introduce sequence data. We then discuss different approaches for mining of patterns from sequence data, studied in... Sample PDF
Approaches for Pattern Discovery Using Sequential Data Mining
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Chapter 96
Human-Centred Web Search  (pages 1852-1872)
Orland Hoeber
People commonly experience difficulties when searching the Web, arising from an incomplete knowledge regarding their information needs, an inability... Sample PDF
Human-Centred Web Search
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Chapter 97
Ana Azevedo, Manuel Filipe Santos
Business Intelligence (BI) is an emergent area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has... Sample PDF
A Perspective on Data Mining Integration with Business Intelligence
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Chapter 98
David Manset
In recent times, innovative new e-Infrastructures have materialized all around the globe to address the compelling and unavoidably increasing demand... Sample PDF
Gridifying Neuroscientific Pipelines: A SOA Recipe and Experience from the neuGRID Project
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Chapter 99
Mingming Zhou, Yabo Xu
A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning... Sample PDF
Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners
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Chapter 100
Fernando Alonso, Loïc Martínez, Aurora Pérez, Juan Pedro Valente
Although expert elicited knowledge and data mining discovered knowledge appear to be completely opposite and competing solutions to the same... Sample PDF
Cooperation between Expert Knowledge and Data Mining Discovered Knowledge
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Chapter 101
Aysegul Cayci, João Bártolo Gomes, Andrea Zanda, Ernestina Menasalvas, Santiago Eibe
Advances in wireless, sensor, mobile and wearable technologies present new challenges for data mining research on providing mobile applications with... Sample PDF
Research Challenge of Locally Computed Ubiquitous Data Mining
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Chapter 102
Klaus Wölfel, Jean-Paul Smets
Free/Open Source software (FOSS) has made Enterprise Resource Planning (ERP) systems more accessible for Small and Medium Enterprises (SMEs)... Sample PDF
Tailoring FOS-ERP Packages: Automation as an Opportunity for Small Businesses
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Chapter 103
José Nava, Paula Hernández
Data mining is a complex process that involves the interaction of the application of human knowledge and skills and technology. This must be... Sample PDF
Optimization of a Hybrid Methodology (CRISP-DM)
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Chapter 104
Leticia Gómez, Bart Kuijpers, Bart Moelans, Alejandro Vaisman
Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic... Sample PDF
A State-of-the-Art in Spatio-Temporal Data Warehousing, OLAP and Mining
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Chapter 105
Zalizah Awang Long, Abdul Razak Hamdan, Azuraliza Abu Bakar, Mazrura Sahani
Today, the objective of public health surveillance system is to reduce the impact of outbreaks by enabling appropriate intervention. Commonly used... Sample PDF
Pattern Mining for Outbreak Discovery Preparedness
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Chapter 106
Stefan Bernhard, Kristine Al Zoukra, Christof Schtte
The past two decades have seen impressive success in medical technology, generating novel experimental data at an unexpected rate. However, current... Sample PDF
From Non-Invasive Hemodynamic Measurements towards Patient-Specific Cardiovascular Diagnosis
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Chapter 107
Marcos Visoli, Sandro Bimonte, Sônia Ternes, François Pinet, Jean-Pierre Chanet
Animal traceability is a very important question for several government and private institutions from many points of view: economical, sanitary... Sample PDF
Towards Spatial Decision Support System for Animals Traceability
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Chapter 108
May Yuan, James Bothwell
The so-called Big Data Challenge poses not only issues with massive volumes of data, but issues with the continuing data streams from multiple... Sample PDF
Space-Time Analytics for Spatial Dynamics
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Chapter 109
Chara Ch. Mitropoulou, Vagelis Plevris, Nikos D. Lagaros
Optimization is a field where extensive research has been conducted over the last decades. Many types of problems have been addressed, and many... Sample PDF
Metaheuristic Optimization in Seismic Structural Design and Inspection Scheduling of Buildings
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Chapter 110
Erkan Polat
Nowadays, analysis of land-use suitability requires consideration of variety of criteria including not only natural/physical capacity of a land unit... Sample PDF
An Approach for Land-Use Suitability Assessment Using Decision Support Systems, AHP and GIS
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Chapter 111
Chengqing Yuan, Xinping Yan, Zhixiong Li, Yuelei Zhang, Chenxing Sheng, Jiangbin Zhao
Marine power machinery parts are key equipments in ships. Ships always work in rigorous conditions such as offshore, heavy load, et cetera.... Sample PDF
Remote Fault Diagnosis System for Marine Power Machinery System
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Chapter 112
Ibrahim George, Manolya Kavakli
In this chapter, the authors explore the operational data related to transactions in a financial organisation to find out the suitable techniques to... Sample PDF
Data Mining in the Investigation of Money Laundering and Terrorist Financing
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Chapter 113
Abdelmalek Amine, Zakaria Elberrichi, Michel Simonet, Ali Rahmouni
The “Molecular Similarity Principle” states that structurally similar molecules tend to have similar properties—physicochemical and biological. The... Sample PDF
A Hybrid Approach Based on Self-Organizing Neural Networks and the K-Nearest Neighbors Method to Study Molecular Similarity
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Chapter 114
Ali Serhan Koyuncugil, Nermin Ozgulbas
After last global financial crisis, one of the most important concerns of the governments became unemployment. Higher unemployment rates haves been... Sample PDF
Social Aid Fraud Detection System and Poverty Map Model Suggestion Based on Data Mining for Social Risk Mitigation
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Chapter 115
Murat Acar, Dilek Karahoca, Adem Karahoca
This chapter focuses on building a financial early warning system (EWS) to predict stock market crashes by using stock market volatility and rising... Sample PDF
Designing an Early Warning System for Stock Market Crashes by Using ANFIS
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Chapter 116
Georgios Patris, Vassilios Vescoukis, Maria Giaoutzi
The present chapter focuses on the Location Based Context-aware Mobile Services (LBCAMS) in the context of the most recent technological advances in... Sample PDF
Advances of the Location Based Context-Aware Mobile Services in the Transport Sector
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