Outlier analysis is used to identify outliers, which are data objects that are inconsistent with the general behavior or model of the data. There are two main types of outlier detection - statistical distribution-based detection, which identifies outliers based on how far they are from the average statistical distribution, and distance-based detection, which finds outliers based on how far they are from other data objects. Outlier analysis is useful for tasks like fraud detection, where outliers may indicate fraudulent activity that is different from normal patterns in the data.

1. Outlier Analysis

2. What are outliers?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.

3. What is Outlier Analysis?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 or outlier analysis.

4. Statistical Distribution-Based Outlier Detection Two basic types of procedures for detecting outliers:Block procedures: In this case, either all of the suspect objects are treated as outliersor all of them are accepted as consistent.Consecutive (or sequential) procedures: An example of such a procedure is the insideoutprocedure.

5. Distance-Based Outlier DetectionSome efficient algorithms for mining distance-based outliers are as follows:Index-based algorithmNested-loop algorithm:Cell-based algorithmDensity-Based Local Outlier DetectionDeviation-Based Outlier Detection with Sequential Exception Technique

6. OLAP Data Cube TechniqueAn OLAP approach to deviation detection uses data cubes to identify regions of anomaliesin large multidimensional data

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