Outlier Detection Techniques for Data Mining

Outlier Detection Techniques for Data Mining

Fabrizio Angiulli (University of Calabria, Italy)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch228
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Data mining techniques can be grouped in four main categories: clustering, classification, dependency detection, and outlier detection. Clustering is the process of partitioning a set of objects into homogeneous groups, or clusters. Classification is the task of assigning objects to one of several predefined categories. Dependency detection searches for pairs of attribute sets which exhibit some degree of correlation in the data set at hand. The outlier detection task can be defined as follows: “Given a set of data points or objects, find the objects that are considerably dissimilar, exceptional or inconsistent with respect to the remaining data”. These exceptional objects as also referred to as outliers. Most of the early methods for outlier identification have been developed in the field of statistics (Hawkins, 1980; Barnett & Lewis, 1994). Hawkins’ definition of outlier clarifies the approach: “An outlier is an observation that deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism”. Indeed, statistical techniques assume that the given data set has a distribution model. Outliers are those points that satisfy a discordancy test, that is, that are significantly far from what would be their expected position given the hypothesized distribution. Many clustering, classification and dependency detection methods produce outliers as a by-product of their main task. For example, in classification, mislabeled objects are considered outliers and thus they are removed from the training set to improve the accuracy of the resulting classifier, while in clustering, objects that do not strongly belong to any cluster are considered outliers. Nevertheless, it must be said that searching for outliers through techniques specifically designed for tasks different from outlier detection could not be advantageous. As an example, clusters can be distorted by outliers and, thus, the quality of the outliers returned is affected by their presence. Moreover, other than returning a solution of higher quality, outlier detection algorithms can be vastly more efficient than non ad-hoc algorithms. While in many contexts outliers are considered as noise that must be eliminated, as pointed out elsewhere, “one person’s noise could be another person’s signal”, and thus outliers themselves can be of great interest. Outlier mining is used in telecom or credit card frauds to detect the atypical usage of telecom services or credit cards, in intrusion detection for detecting unauthorized accesses, in medical analysis to test abnormal reactions to new medical therapies, in marketing and customer segmentations to identify customers spending much more or much less than average customer, in surveillance systems, in data cleaning, and in many other fields.
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Approaches to outlier detection can be classified in supervised, semi-supervised, and unsupervised.

Supervised methods exploit the availability of a labeled data set, containing observations already labeled as normal and abnormal, in order to build a model of the normal class. Since usually normal observations are the great majority, these data sets are unbalanced and specific classification techniques must be designed to deal with the presence of rare classes (Chawla et al., 2004).

Semi-supervised methods assume that only normal examples are given. The goal is to find a description of the data, that is a rule partitioning the object space into an accepting region, containing the normal objects, and a rejecting region, containing all the other objects. These methods are also called one-class classifiers or domain description techniques, and they are related to novelty detection since the domain description is used to identify objects significantly deviating form the training examples.

Unsupervised methods search for outliers in an unlabelled data set by assigning to each object a score which reflects its degree of abnormality. Scores are usually computed by comparing each object with objects belonging to its neighborhood.

Data mining researchers have largely focused on unsupervised approaches. Most of the unsupervised approaches proposed in the data mining literature can be classified as deviation-based (Arning et al., 1996), distance-based (Knorr & Ng, 1998), density-based (Breunig et al., 2000), and MDEF-based (Papadimitriou et al., 2003).

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