Data Mining Techniques for Outlier Detection

Data Mining Techniques for Outlier Detection

N Suri (C V Raman Nagar, India), M Murty (Indian Institute of Sceince, India) and G Athithan (C V Raman Nagar, India)
Copyright: © 2013 |Pages: 20
DOI: 10.4018/978-1-4666-2455-9.ch009
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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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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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Table of Contents
Chapter 1
Sangeetha Kutty, Richi Nayak, Tien Tran
With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from... Sample PDF
A Study of XML Models for Data Mining: Representations, Methods, and Issues
Chapter 2
Anthony Scime, Karthik Rajasethupathy, Kulathur S. Rajasethupathy, Gregg R. Murray
Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in... Sample PDF
Finding Persistent Strong Rules: Using Classification to Improve Association Mining
Chapter 3
Frederick E. Petry
This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining... Sample PDF
Data Discovery Approaches for Vague Spatial Data
Chapter 4
Laurent A. Baumes
The data mining technology increasingly employed into new industrial processes, which require automatic analysis of data and related results in... Sample PDF
Active Learning and Mapping: A Survey and Conception of a New Stochastic Methodology for High Throughput Materials Discovery
Chapter 5
Harleen Kaur, Ritu Chauhan, M. Alam
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An Optimal Categorization of Feature Selection Methods for Knowledge Discovery
Chapter 6
Huiwei Guan
Distributed computing and Peer-to-Peer (P2P) systems have emerged as an active research field that combines techniques which cover networks... Sample PDF
Parallel Computing for Mining Association Rules in Distributed P2P Networks
Chapter 7
Fernando Benites, Elena Sapozhnikova
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Learning Different Concept Hierarchies and the Relations between them from Classified Data
Chapter 8
Baoying Wang, Aijuan Dong
Clustering and outlier detection are important data mining areas. Online clustering and outlier detection generally work with continuous data... Sample PDF
Online Clustering and Outlier Detection
Chapter 9
N Suri, M Murty, G Athithan
Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Detecting... Sample PDF
Data Mining Techniques for Outlier Detection
Chapter 10
Ramón A. Carrasco, Miguel J. Hornos, Pedro Villar, María A. Aguilar
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An Extraction, Transformation, and Loading Tool Applied to a Fuzzy Data Mining System
Chapter 11
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Analysis and Integration of Biological Data: A Data Mining Approach using Neural Networks
Chapter 12
T. T. Wong, Loretta K.W. Sze
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Chapter 13
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A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I: Sequences of Continuous Feature Vectors
Chapter 14
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Chapter 15
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Measuring Human Intelligence by Applying Soft Computing Techniques: A Genetic Fuzzy Approach
Chapter 16
Roberto Marmo
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Web Mining and Social Network Analysis
Chapter 17
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Long-Term Evolution (LTE): Broadband-Enabled Next Generation of Wireless Mobile Cellular Network
Chapter 18
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Soft Computing and its Applications
Chapter 19
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A Particle Filtering Based Approach for Gear Prognostics
Chapter 20
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Chapter 21
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Chapter 22
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Chapter 23
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Chapter 25
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Chapter 27
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Chapter 29
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Chapter 30
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Chapter 34
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Modeling, Querying, and Mining Uncertain XML Data
Chapter 35
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Database Analysis with ANNs by means of Graph Evolution
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Chapter 39
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Data Mining System Execution Traces to Validate Distributed System Quality-of-Service Properties
Chapter 40
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Mammogram Mining Using Genetic Ant-Miner
Chapter 41
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A Dynamic Privacy Manager for Compliance in Pervasive Computing
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Heterogeneous Text and Numerical Data Mining with Possible Applications in Business and Financial Sectors
Chapter 43
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Chapter 44
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Chapter 45
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Chapter 47
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Chapter 48
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Chapter 49
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A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis
Chapter 50
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Cloud-Based Intelligent DSS Design for Emergency Professionals
Chapter 51
Dario Antonelli, Elena Baralis, Giulia Bruno, Silvia Chiusano, Naeem A. Mahoto, Caterina Petrigni
With the introduction of electronic medical records, a large amount of patients’ medical data has been available. An actual problem in this domain... Sample PDF
Extraction of Medical Pathways from Electronic Patient Records
Chapter 52
Pratibha Rani, Vikram Pudi
The rapid progress of computational biology, biotechnology, and bioinformatics in the last two decades has led to the accumulation of tremendous... Sample PDF
Classification of Biological Sequences
Chapter 53
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Chapter 54
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Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the... Sample PDF
Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support
Chapter 55
Suzanne Bakken, Robert Lucero, Sunmoo Yoon, Nicholas Hardiker
A sound informatics infrastructure is essential to optimise the application of evidence in nursing practice. A comprehensive review of the... Sample PDF
Implications for Nursing Research and Generation of Evidence
Chapter 56
Jianhua Yao, Ronald M. Summers
The growing repositories of clinical imaging data generate a need for effective image management and access that demands more than simple text-based... Sample PDF
Content-Based Image Retrieval for Medical Image Analysis
Chapter 57
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Intelligent Decision Support System for Fetal Delivery using Soft Computing Techniques
Chapter 58
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Systems Biology-Based Approaches Applied to Vaccine Development
Chapter 59
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From Data to Knowledge: Data Mining
Chapter 60
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Chapter 61
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For large-scale data mining, utilizing data from ubiquitous and mixed-structured data sources, the extraction and integration into a comprehensive... Sample PDF
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Chapter 62
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Chapter 63
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Chapter 64
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Chapter 65
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Chapter 66
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Chapter 67
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Chapter 68
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Chapter 69
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Chapter 73
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Chapter 74
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Chapter 75
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Chapter 76
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Chapter 90
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Chapter 91
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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
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
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
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)
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
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
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
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
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
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
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
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
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
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
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
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
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