Extreme Value AnalysisĮxtreme Value Analysis is the most basic form of outlier detection and great for 1-dimension data. Outlier detection models may be classified into the following groups: 1. There are several approaches to detecting Outliers. Outlier Detection Methods Models for Outlier Detection Analysis Again, some Outlier Techniques require a distance measure, and some the calculation of mean and standard deviation. ![]() Some of the techniques may require normalization and a Gaussian distribution of the inspected dimension. Some may work for one-dimensional feature spaces, while others may work well for low dimensional spaces, and some extend to high dimensional spaces. The aforementioned Outlier Techniques are the numeric outlier, z-score, DBSCAN and isolation forest methods. The outliers are the data points that are in the tails of the distribution and therefore far from the mean. Z-score technique assumes a Gaussian distribution of the data. This technique can easily be implemented in KNIME Analytics Platform using the Numeric Outliers node. Using the interquartile multiplier value k=1.5, the range limits are the typical upper and lower whiskers of a box plot. An outlier is then a data point xi that lies outside the interquartile range. For example, the first and the third quartile (Q1, Q3) are calculated. The outliers are calculated by means of the IQR (InterQuartile Range). Numeric Outlier is the simplest, nonparametric outlier detection technique in a one-dimensional feature space. There are four Outlier Detection techniques in general. ![]() (ii) Can I assume a distribution(s) of values for my selected features? (parametric / non-parametric) (i) Which and how many features am I considering for outlier detection? (univariate / multivariate) Remember two important questions about your dataset in times of outlier identification: ![]() Outlier Detection as a branch of data mining has many applications in data stream analysis.ĭefinition of outlier detection Outlier Detection Techniquesįor 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. There are no standardized Outlier identification methods as these are largely dependent upon the data set. Therefore, Outlier Detection may be defined as the process of detecting and subsequently excluding outliers from a given set of data. An outlier may be caused simply by chance, but it may also indicate measurement error or that the given data set has a heavy-tailed distribution. There is no rigid mathematical definition of what constitutes an outlier determining whether or not an observation is an outlier is ultimately a subjective exercise.Īn outlier may also be explained as a piece of data or observation that deviates drastically from the given norm or average of the data set. Outliers are generally defined as samples that are exceptionally far from the mainstream of data.
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