Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Over the decades, traditional outlier detectors have ignored the group-level factor when
calculating outlier scores for objects in data by evaluating only the object-level factor, failing …

Detecting outliers in data is an important problem with interesting applications in a myriad of
domains ranging from data cleaning to financial fraud detection and from network intrusion …

Outlier detection targets those exceptional data that deviate from the general pattern.
Besides high density clustering, there is another pattern called low density regularity. Thus …

Outlier detection has been a very important concept in the realm of data analysis. Recently,
several application domains have realized the direct mapping between outliers in data and …

Outlier detection is a hot topic in machine learning. With the newly emerging technologies
and diverse applications, the interest of outlier detection is increasing greatly. Recently, a …

Unsupervised k-nearest-neighbor-based outlier detectors play a vital role in data science
research. However, the detectors' performance relies on the choice of the parameter k …

Outlier detection is crucial for improving the performance of machine learning algorithms
and is particularly vital in data sets possessing a small number of points. While the existing …

Outlier detection aims at searching for a small set of objects that are inconsistent or
considerably deviating from other objects in a dataset. Existing research focuses on outlier …