Abstract: Problem statement: Methods proposed for estimating and resolving outliers are compared. Approach: In this respect, we exploit three well-known classifiers for identifying outliers to establish guidelines for the choice of outlier detection methods. Results: It was shown that the standard deviation is inappropriate to use here because it is highly sensitive to extreme values. Conclusion/Recommendations: The result of these estimated outliers is a better way of resolving large population
Data Mining just alludes to the extraction of exceptionally intriguing patterns of the data from the...
This paper deals with finding outliers (exceptions) in large datasets. The identification of outlier...
In this paper, the task of identifying outliers in exponential samples is treated conceptionally in ...
Most real-world data sets contain outliers that have unusually large or small values when compared w...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...
An outlier is an observation that appears to deviate markedly from other observations in the sample ...
The problem of outliers in statistical data has attracted many researchers for a long time. Conseque...
In survey sampling theory, the interest usually lies in the estimation of finite population paramete...
This tutorial has four aims: (1) Providing the current comparative works on different outlier detect...
Data Mining simply refers to the extraction of very interesting patterns of the data from the massiv...
Outlier detection can be viewed as a classification problem if a training data set with class labels...
While the utilisation of different methods of outliers correction has been shown to counteract the i...
This paper introduces two statistical outlier detection approaches by classes. Experiments on binar...
One approach to identifying outliers is to assume that the outliers have a different distribution fr...
In this paper we propose a method for correctly detecting outliers based on a new technique develope...
Data Mining just alludes to the extraction of exceptionally intriguing patterns of the data from the...
This paper deals with finding outliers (exceptions) in large datasets. The identification of outlier...
In this paper, the task of identifying outliers in exponential samples is treated conceptionally in ...
Most real-world data sets contain outliers that have unusually large or small values when compared w...
Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even mor...
An outlier is an observation that appears to deviate markedly from other observations in the sample ...
The problem of outliers in statistical data has attracted many researchers for a long time. Conseque...
In survey sampling theory, the interest usually lies in the estimation of finite population paramete...
This tutorial has four aims: (1) Providing the current comparative works on different outlier detect...
Data Mining simply refers to the extraction of very interesting patterns of the data from the massiv...
Outlier detection can be viewed as a classification problem if a training data set with class labels...
While the utilisation of different methods of outliers correction has been shown to counteract the i...
This paper introduces two statistical outlier detection approaches by classes. Experiments on binar...
One approach to identifying outliers is to assume that the outliers have a different distribution fr...
In this paper we propose a method for correctly detecting outliers based on a new technique develope...
Data Mining just alludes to the extraction of exceptionally intriguing patterns of the data from the...
This paper deals with finding outliers (exceptions) in large datasets. The identification of outlier...
In this paper, the task of identifying outliers in exponential samples is treated conceptionally in ...