An outlier is an object that deviates significantly from the rest of the objects. They can be caused by measurement or execution error. The analysis of outlier data is referred to as outlier analysis or outlier mining. An outlier is an element of a data set that distinctly stands out from the rest of the data.
There are also different degrees of outliers:
- Extreme outliers are beyond an “outer fence.”
- Mild outliers lie beyond an “inner fence” on either side.
The easiest way to detect outliers is to create a graph. Plots such as Box plots, Scatterplots and Histograms can help to detect outliers. Alternatively, we can use mean and standard deviation to list out the outliers. Interquartile Range and Quartiles can also be used to detect outliers.
Clustering based outlier detection using distance to the closest cluster:
In the K-Means clustering technique, each cluster has a mean value. Objects belong to the cluster whose mean value is closest to it. In order to identify the Outlier, firstly we need to initialize the threshold value such that any distance of any data point greater than it from its nearest cluster identifies it as an outlier for our purpose. Then we need to find the distance of the test data to each cluster mean. Now, if the distance between the test data and the closest cluster to it is greater than the threshold value then we will classify the test data as an outlier.
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How to detect outliers in data
Data visualization is a core discipline for analysts and optimizers, not just to better communicate results with executives, but to explore the data fully.
As such, outliers are often detected through graphical means, though you can also do so by a variety of statistical methods using your favorite tool. (Excel and R will be referenced heavily here, though SAS, Python, etc., all work).
Two of the most common graphical ways of detecting outliers are the boxplot and the scatterplot. A boxplot is my favorite way.
- Calculate the mean of each cluster
- Initialize the Threshold value
- Calculate the distance of the test data from each cluster mean
- Find the nearest cluster to the test data
- If (Distance > Threshold) then, Outlier