Outlier Detection in Data Mining

Outlier detection is a primary step in many data-mining applications. In many data analysis tasks a large number of variables are being recorded or sampled. One of the first steps towards obtaining a coherent analysis is the detection of outlaying observations. Although outliers are often considered as an error or noise, they may carry important information. Detected outliers are candidates for aberrant data that may otherwise adversely lead to model misspecification, biased parameter estimation and incorrect results. It is therefore important to identify them prior to modeling and analysis. Outlier Detection Overview Outlier Detection is an algorithmic feature that allows you to detect when some members of a group are behaving strangely compared to the others. Outlier detection is an important research problem in data mining that aims to find objects that are considerably dissimilar, exceptional and inconsistent with respect to the majority of the data in an input database. Outliers are extreme values that deviate from other observations on data; they may indicate variability in a measurement, experimental errors or a novelty. An outlier is an observation (or measurement) that is different with respect to the other values contained in a given dataset. Outliers can be due to several…

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