Data analysis and mining involve evaluating data to identify the trends and patterns in the data. There are some cases when outliers emerge in the data set. Outlier refers to the deviation in the data set when one or more than one values deviate from the standard.
The process of analyzing these outliers refers to as the analysis of outlier in data mining. Outlier analysis focuses on examining the noisy data, which means an examination of the anomalies that emerge during data analysis and mining.
Outliers can occur during the data analysis phase, and therefore, it is important for researchers to know how to do outlier analysis. This article will tell you about the analysis of outliers and their types and importance.
What is Outlier analysis in Data Mining?
Before delving into the details of outlier analysis, let us first try to understand what causes outliers in the data set. There are several reasons that an outlier can occur in a data set. One of the primary reasons is that researchers do not enter the data properly.
It can also be due to measurement errors and computational errors. Researchers often commit sampling error that leads to outliers in the data sets. Outliers are the anomalies that occur in the data set.
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So, analysis of outliers is a technique that identifies the abnormalities in the data sets. It identifies and discovers the anomalies that occur in a data set. Anomalies in the data set are also commonly known as noisy data.
What is the importance of outlier analysis in Data Mining?
Analysis of outliers has practical implications for banks and companies to detect frauds. The most common application of outlier analysis is in the banking, insurance, and health sector.
Analysis of outlier detects the frauds and scams concerning credit cards, insurance cards, and healthcare. Analysis of outliers is also important in the telecommunication sector for fraud detection.
Furthermore, it is important in the safety programs implemented by industries and factories. Governmental nuclear plants that monitor the safety hazards 24/7 use analysis of outliers to detect any anomalies in the data.
Business and marketing policies take help from the analysis of outliers to understand consumer patterns. In medical and clinical research, whenever any new treatment or medicine is introduced, analysis of outlier helps researchers to identify anomalies.
What are the different types of outlier analysis?
There are three different types of outlier analysis. According to the degree of deviation from the data set, these three types of outliers are identified in the analysis phase.

Global Outlier analysis
Global outliers’ analysis refers to the data points that diverge far from the data set. The researchers identify the global outlier by observing data points. A global outlier is a data point that deviates from every other data point in a dataset.

Collective Outlier analysis
Collective outlier analysis identifies more than one data point that deviates from the data set. These data points are more than one, and they all deviate at one point in time from the data set.
Identifying and observing more than one deviated data point is a collective outlier analysis. Researchers observe and examine the fundamental nature of those data points that exhibit outlier behavior. This technique helps them perform collective outlier examinations.

Contextual Outlier analysis
Contextual outlier analysis is also referred to as conditional outliers. Conditional or contextual analysis of outlier identifies those data points that deviate based on certain conditions or contexts only. The context or condition of all the deviated data points is similar. Researchers identify the conditions that cause the data point to behave abnormally.
A data point in a given data set might deviate in a particular condition but behave normally in other conditions. So, in medical research, behavioral conditions and contexts help researchers understand the behavior of patients effectively.
What are the different techniques for doing outlier analysis?
There are several techniques you can use to do an analysis of outliers. Here are some of the common techniques.

Sorting
The first important technique researchers can use to analyze outliers in data mining is sorting. Sorting refers to the filtration or cleaning phase of the data. Researchers look for any wrong entries or computational errors that might result in potential outliers. It also requires the classification of data according to their magnitude.
Furthermore, it requires the organization of data into different ranges, such as the higher range or, the lower range. Data points that are relatively higher than other data points are identified as outliers.

Graphing technique
Graphing technique is also useful in doing analysis of outliers. You can do the following things:
 First of all, plot the entire data points on a graph
 Identify the higher and lower range of the data points
 Identify which data points have relatively higher values than other data points
 Graphic technique will help you identify the magnitude of several data points
 You can use histograms and scatter plots to identify and analyse the outliers

ZScore Technique
The Zscore technique will help you do an analysis of outliers in data mining. You can use Zscore to determine which data points are showing deviation from the data set’s mean. Standard deviation values will help you identify the deviation of data points from the mean.
Zscore helps you identify the anomalies in the data set. A Zscore value with a positive value of 2 indicates that the data point is deviating two positive points above the sample’s mean. A Zscore value with a negative value of 2 shows that the data point deviates two points below the mean.

Isolation forests
The isolation forest technique involves the usage of binary forests to analyze data outliers in a data set. Binary forests are random forests that analyze all values and identify the deviation values. The process further entails the random splitting method that picks the values deviating from the mean.
Conclusion
Outlier analysis is a useful technique for identifying fraud and data anomalies in health treatments. Researchers can detect anomalies and prevent fraud detection. Safety management teams have to look out for outliers constantly. Analysis of outliers helps them in managing and implementing the safety protocols efficiently.