Add to ORKGIn many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that we start with some "normal" values x 1 ; : : : ; xn , compute the sample average E, the sample standard variation oe, and then mark a value x as an outlier if x is outside the k 0 -sigma interval [E \Gamma k 0 \Delta oe; E + k 0 \Delta oe] (for some pre-selected parameter k 0 ). In real life, we often have only interval ranges [x i ; x i ] for the normal values x 1 ; : : : ; xn . In this case, we only have intervals of possible values for the bounds E \Gamma k 0 \Delta oe and E + k 0 \Delta oe. We can therefore identify outliers as values that are outside all k 0 -sigma intervals
Outlier detection refers to the detection of unexpected situations in the data. Outliers are fraud, ...
Outlier Detection is a technique to detect anomalous events or outliers during analysis of the data ...
This paper deals with finding outliers (exceptions) in large datasets. The identification of outlier...
In many application areas, it is important to detect outliers. The traditional engineering approach ...
In many application areas, it is important to detect outliers. Traditional engineering approach to o...
In many application areas it is important to detect outliers. Traditional engineering approach to ou...
All in-text references underlined in blue are linked to publications on ResearchGate, letting you ac...
In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an...
In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an...
In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an...
A multivariate outlier detection method for interval data is proposed that makes use of a parametric...
Outlier analysis is that the user do depends on the kinds data they have. An outlier is a data value...
Outliers are often ubiquitous in surveys that involve linear measurements. Knowing that the presence...
Outlier detection has become an important data mining problem in many applications, including custom...
An outlier is an observation that appears to deviate markedly from other observations in the sample ...
Outlier detection refers to the detection of unexpected situations in the data. Outliers are fraud, ...
Outlier Detection is a technique to detect anomalous events or outliers during analysis of the data ...
This paper deals with finding outliers (exceptions) in large datasets. The identification of outlier...
In many application areas, it is important to detect outliers. The traditional engineering approach ...
In many application areas, it is important to detect outliers. Traditional engineering approach to o...
In many application areas it is important to detect outliers. Traditional engineering approach to ou...
All in-text references underlined in blue are linked to publications on ResearchGate, letting you ac...
In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an...
In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an...
In the classical leave-one-out procedure for outlier detection in regression analysis, we exclude an...
A multivariate outlier detection method for interval data is proposed that makes use of a parametric...
Outlier analysis is that the user do depends on the kinds data they have. An outlier is a data value...
Outliers are often ubiquitous in surveys that involve linear measurements. Knowing that the presence...
Outlier detection has become an important data mining problem in many applications, including custom...
An outlier is an observation that appears to deviate markedly from other observations in the sample ...
Outlier detection refers to the detection of unexpected situations in the data. Outliers are fraud, ...
Outlier Detection is a technique to detect anomalous events or outliers during analysis of the data ...
This paper deals with finding outliers (exceptions) in large datasets. The identification of outlier...