This chapter describes anomaly detection, an unsupervised mining function for detecting rare cases in the data. Campos, M.
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The goal of anomaly detection is to identify cases that are unusual within data that is seemingly homogeneous. Anomaly detection is an important tool for detecting fraud, network intrusion, and other rare events that may have great ificance but are hard to find. A law enforcement agency seekung data about illegal activities, but nothing about legitimate activities.
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How can suspicious activity be flagged? An insurance agency processes millions of insurance claims, knowing that a very small are fraudulent.
How can the fraudulent claims be identified? Anomaly detection is a form of classification. See "About Classification" on for an overview of the classification mining function.
About anomaly detection
Anomaly detection is implemented as one-class classification, because only one class is represented in the training data. An anomaly detection model predicts whether a data point is typical for a given distribution or not.
An atypical data point can be either an outlier or an example of a ly unseen class. Normally, a classification model must be trained on data that includes both examples and counter-examples for each class so that the model can learn to distinguish between them. For example, a model that predicts side effects of a medication should be trained on data that includes a wide anomalt of responses to the medication. A one-class classifier develops a profile that generally describes a typical case in the training data.
Deviation from the profile is identified as an anomaly.
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One-class classifiers are sometimes referred to as positive security models, because they seek to identify "good" behaviors and aomaly that all other behaviors are bad. The goal of anomaly detection is to provide some useful information where no information was ly attainable.
However, if there are enough of the "rare" cases so that stratified sampling could produce a training set with enough counterexamples for a standard classification model, then that would generally be a better solution. In single-class data, all the cases have the same classification.
Counter-examples, instances of another class, may be hard to specify or expensive to collect. For instance, in text document classification, it may be easy to classify a document under a given topic.
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However, the universe of documents outside of this topic may be very large and diverse. Thus it may not be feasible to specify other types of documents as counter-examples. Outliers are cases that are unusual because they fall outside the distribution that is anojaly normal for the data.
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These cases would probably be identified as outliers. The distance from the center of a normal distribution indicates how typical a given point is with respect to the distribution of the data.
Each case can be ranked according to anomzly probability that it is either typical or atypical. The presence of outliers can have a deleterious effect on many forms of data mining. Anomaly detection can be used to identify outliers before mining the data. These examples show how anomaly detection might be used to find outliers in the training data or to score new, single-class data.
Figure shows six columns and ten rows from the case table used to build the model. Note that no column is deated as a target, because the data is all of one class.
Suppose you want to create a data set consisting of demographic data for typical customers. You might start by identifying the most atypical customers and removing them from the data. To find the outliers, you can use the anomaly detection model to score the build data. Figure shows that customeris anomalous and should be removed. Suppose that you have a new anomwly, and you want to evaluate how closely he resembles a typical customer in your current customer database.
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You can use the anomaly detection model to score the new customer data. The new customer is a year-old male executive who has a bachelors degree and uses an affinity card.
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The function returns. The new customer is somewhat of an anomaly. When used for anomaly detection, SVM classification does not use a target.
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See Also: "Unsupervised Data Mining". Reference: Campos, M. About Anomaly Detection The goal of anomaly detection is to identify cases that sseking unusual within data that is seemingly homogeneous. Anomaly detection can be used to solve problems like the following: A law enforcement agency compiles data about illegal activities, but nothing about legitimate activities.
In fact, once anomalies become publicly known, they tend to quickly disappear as arbitragers seek out and eliminate any such opportunity from. This transform should only be used when looking for mean anomalies. CAPA - Simulated data. To demonstrate observ.club, a univariate data. Anomaly detection refers to finding patterns or instances in data that do not con- form to what is normal and expected, i.e. anomalies are rare and different from.
The law enforcement data is all of one class. There are no counter-examples.
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The claims data contains very few counter-examples. They are outliers. One-Class Classification Anomaly detection is a form of classification. Note: Solving a one-class classification problem can be difficult.
The accuracy of one-class classifiers cannot usually match the accuracy of standard classifiers built with meaningful counterexamples. Anomaly Detection for Single-Class Data In single-class data, all the cases have the same classification. Anomaly detection could be used to find unusual instances of a particular type of document. Anomaly Detection for Finding Outliers Outliers are cases that are unusual because they fall outside the distribution that is considered normal for the data.
Sample Anomaly Detection Problems These examples show how anomaly detection might be used to find outliers in the training data or to score new, single-class data. Seeeking Find Outliers Suppose you want to create a data set consisting of demographic data for typical customers. Note: A prediction of 0 is considered anomalous.
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A prediction of 1 is considered typical. A "1" is appended to the column name of each predictor that you choose to include in the output. Example: Score New Data Suppose that you have a new customer, and you want to evaluate how closely he resembles a typical customer in your current customer database.