Introduction: Very often, there exist data objects that do not comply with the general behavior or model of the data. Such data objects, which are grossly different from or inconsistent with the remaining set of data, are called outliers.
Outliers can be caused by measurement or execution error. For example, the display of a person’s age as -999 could be caused by a program default setting of an unrecorded age. Alternatively, outliers may be the result of inherent data variability. The salary of the chief executive officer of a company, for instance, could naturally stand out as an outlier among the salaries of the other employees in the firm.
Many data mining algorithms try to minimize the influence of outliers or eliminate them all together. This, however, could result in the loss of important hidden information because one person’s noise could be another person’s signal. In other words, the outliers may be of particular interest, such as in the case of fraud detection, where outliers may indicate fraudulent activity. Thus, outlier detection and analysis is an interesting data mining task, referred to as outlier mining.
Outlier mining has wide applications. As mentioned previously, it can be used in fraud detection, for example, by detecting unusual usage of credit cards or telecommunication services. In addition, it is useful in customized marketing for identifying the spending behavior of customers with extremely low or extremely high incomes, or in medical analysis for finding unusual responses to various medical treatments.
Outlier mining can be described as follows: Given a set of n data points or objects and k, the expected number of outliers, find the top k objects that are considerably dissimilar, exceptional, or inconsistent with respect to the remaining data. The outlier mining problem can be viewed as two sub problems: (1) define what data can be considered as inconsistent in a given data set, and (2) find an efficient method to mine the outliers so defined.
The problem of defining outliers is nontrivial. If a regression model is used for data modeling, analysis of the residuals can give a good estimation for data “extremeness.” The task becomes tricky, however, when finding outliers in time-series data, as they may be hidden in trend, seasonal, or other cyclic changes. When multidimensional data are analyzed, not any particular one but rather a combination of dimension values may be extreme. For nonnumeric (i.e., categorical) data, the definition of outliers requires special consideration.
Using data visualization methods for outlier detection: This may seem like an obvious choice, since human eyes are very fast and effective at noticing data inconsistencies. However, this does not apply to data containing cyclic plots, where values that appear to be outliers could be perfectly valid values in reality. Data visualization methods are weak in detecting outliers in data with many categorical attributes or in data of high dimensionality, since human eyes are good at visualizing numeric data of only two to three dimensions.
In this section, we instead examine computer-based methods for outlier detection. These can be categorized into four approaches: the statistical approach, the distance-based approach, the density-based local outlier approach, and the deviation-based approach, each of which are studied here. Notice that while clustering algorithms discard outliers as noise, they can be modified to include outlier detection as a by-product of their execution. In general, users must check that each outlier discovered by these approaches is indeed a “real” outlier.
