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Data Mining: Outlier analysis
 

Data Mining: Outlier analysis

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Data Mining: Outlier analysis

Data Mining: Outlier analysis

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    Data Mining: Outlier analysis Data Mining: Outlier analysis Presentation Transcript

    • Outlier Analysis
    • 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.
    • 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.
    • 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.
    • Distance-Based Outlier Detection
      Some efficient algorithms for mining distance-based outliers are as follows:
      Index-based algorithm
      Nested-loop algorithm:
      Cell-based algorithm
      Density-Based Local Outlier Detection
      Deviation-Based Outlier Detection with Sequential Exception Technique
    • OLAP Data Cube Technique
      An OLAP approach to deviation detection uses data cubes to identify regions of anomaliesin large multidimensional data
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