https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623
https://towardsdatascience.com/dbscan-algorithm-complete-guide-and-application-with-python-scikit-learn-d690cbae4c5d

Ways of detecting outliers

Isolation forests isolate outliers found using anomaly detection. This is adverse to other techniques that typically use data profiling (which are statistical methods used to describe the data set as a whole).


Isolation  Forest

Robust random cut forests also isolate outliers, however they do so through association. It associates a score to an anomaly, thereby analyzing these scores on a basis of “normal”, “low” or “high”. It works extremely well on robust data. 


Robust random cut forests 


There are many statistical approaches besides boxplots that include (but are not limited to): z-score, standard deviation, etc.
Box Plots create a visual of the spread of data. It automatically marks what it all,analyzes as an anomaly.


Boxplot/Statistical Approach

- Isolation forest
- Robust random cut forest 
- Boxplots/Statistical Approach


University of Michigan - Ann Arbor

Finding unusual patterns in the data

https://www.sciencedirect.com/topics/computer-science/outlier-detection

Outlier detection

Outlier: data that differ dramatically from all others and distinguish themselves in characteristics.

Method 1 — Standard Deviation: data point that is more than 3 times the standard deviation, then those points are very likely to be outliers

Method 2 — Boxplots: Any data points that show above or below the whiskers, can be considered outliers.

Method 3— DBScan Clustering: Three core concepts (points) in DBScan:
Core Points
Border Points
Everything else is called Noise Points

(https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623)

3 Methods to detect Outlier. 

WHAT IS OUTLIER DETECTION: identification of points that are extreme in value

Outlier: large residual / departure in the vertical dimension

Leverage point: the extreme value of predictor variable / big difference in the horizontal dimension

Influence point: Removing observation substantially by changing estimate of coefficient

Outliers, Leverage Points and Influential Points


Which of the following methods is most suitable for anomaly detection in n (n > 1) dimensional space？

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