The outlier sum (OS) statistic is intended to detect a difference between two statistical distributions that is concentrated in one or both tails of the distributions. The outlier-detection classification model that is built based on the test dataset can predict whether the unknown data is an outlier or not. Therefore a study needs to be made before an outlier is discarded. Outlier data may be difficult to source because they are rare. (2013). Determining Outliers . Multiplying the interquartile range (IQR) by 1.5 will give us a way to determine whether a certain value is an outlier. Conway, J. H., & Sloane, N. J. For outlier identification in a dataset, it is very important to keep in mind the context and finding answer the very basic and pertinent question: “Why do I want to detect outliers?” The context will explain the meaning of your findings. Outliers in data can distort predictions and affect the accuracy, if you don’t detect and handle them appropriately especially in regression models. Global outlier — Object significantly deviates from the rest of the data set 2. Deletion of Values: When there are legitimate errors and cannot be corrected, or lie so far outside the range of the data that they distort statistical inferences the outliers should be deleted. The interquartile range IQR = 50 – 40 = 10. The adverse effects of outliers could even influence the life of citizens when data collected by the government contains outliers. 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Article  After updating the model for \(co_c\), the main processing procedure adds neighbourhood from \(Q_n\) to the same \(co_c\) and initiates the processing of the neighbourhood. If the neighbour n is redirected to component co and its population falls behind the population of co (27), we mark this neighbour as obsolete. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1449–1461. Outlier Detection Techniques. All current classification objects \(k = |Co| = k_c + k_o\) can be divided into components \(k_c = |Cm|\) and outliers \(k = |O|\). Gama, J., & Gaber, M. M. (2007). longitudinal data) using SAS. In 2014 IEEE 30th international conference on data engineering (ICDE), IEEE (pp. 443–448). Anomaly detection is a hard data analysis process that requires constant creation and improvement of data analysis algorithms. M-tree: An efficient access method for similarity search in metric spaces. (1950). Note on a method for calculating corrected sums of squares and products. 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