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 …

High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …

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 …

An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

Outlier detection research has been seeing many new algorithms every year that often
appear to be only slightly different from existing methods along with some experiments that …

Outlier detection and ensemble learning are well established research directions in data
mining yet the application of ensemble techniques to outlier detection has been rarely …

Outlier detection research is currently focusing on the development of new methods and on
improving the computation time for these methods. Evaluation however is rather heuristic …

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 …

An outlier is an event or observation that is defined as an unusual activity, intrusion, or a
suspicious data point that lies at an irregular distance from a population. The definition of an …