Comparative Study of Outlier Detection Algorithms

As the dimension of the data is increasing day by day, outlier detection is emerging as one of the active area of research. Finding of the outliers from large data sets is the main problem. Outlier is considered as the pattern that is different from the rest of the patterns present in the data set. The detection of the outlier in the data set is an important process as it helps in acquiring the useful information that further helps in the data analysis. Various algorithms have been proposed till date for the detection of the outliers. This paper covers a study of various outlier detection algorithms like Statistical based outlier detection, Depth based outlier detection, Clustering based technique, Density based outlier detection etc. Comparison study of these outlier detection methods is done to find out which of the outlier detection algorithms are more applicable on high dimensional data.

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