This is the simplest, nonparametric outlier detection method in a one dimensional feature space. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. Mathematically, any observation far removed from the mass of data is classified as an outlier. Four Outlier Detection Techniques Numeric Outlier. If you want to draw meaningful conclusions from data analysis, then this step is a must.Thankfully, outlier analysis is very straightforward. It becomes essential to detect and isolate outliers to apply the corrective treatment. DATABASE SYSTEMS GROUP Statistical Tests • A huge number of different tests are available differing in – Type of data distribution (e.g. Gaussian) – Number of variables, i.e., dimensions of the data objects Outlier analysis is a data analysis process that involves identifying abnormal observations in a dataset. In practice, outliers could come from incorrect or inefficient data gathering, industrial machine malfunctions, fraud retail transactions, etc. Here outliers are calculated by means of the IQR (InterQuartile Range). Figure 2: A Simple Case of Change in Line of Fit with and without Outliers The Various Approaches to Outlier Detection Univariate Approach: A univariate outlier is a … If a single observation is more extreme than either of our outer fences, then it is an outlier, and more particularly referred to as a strong outlier.If our data value is between corresponding inner and outer fences, then this value is a suspected outlier or a weak outlier. High-Dimensional Outlier Detection: Specifc methods to handle high dimensional sparse data; In this post we briefly discuss proximity based methods and High-Dimensional Outlier detection methods. Outlier detection is the process of detecting and subsequently excluding outliers from a given set of data. Outliers can now be detected by determining where the observation lies in reference to the inner and outer fences. The first and the third quartile (Q1, Q3) are calculated. By default, we use all these methods during outlier detection, then normalize and combine their results and give every datapoint in the index an outlier score. Aggarwal comments that the interpretability of an outlier model is critically important. High-Dimensional Outlier Detection: Methods that search subspaces for outliers give the breakdown of distance based measures in higher dimensions (curse of dimensionality). The outlier score ranges from 0 to 1, where the higher number represents the chance that the data point is an outlier … Kriegel/Kröger/Zimek: Outlier Detection Techniques (KDD 2010) 18. Information Theoretic Models: The idea of these methods is the fact that outliers increase the minimum code length to describe a data set. 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