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

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 …

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 …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

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 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 …

Anomaly analysis is of great interest to diverse fields, including data mining and machine
learning, and plays a critical role in a wide range of applications, such as medical health …

The performance of similarity measures for search, indexing, and data mining applications
tends to degrade rapidly as the dimensionality of the data increases. The effects of the so …

A number of authors have suggested the use of argumentation techniques as the basis for
negotiation dialogues between agents. In this paper we augment this work by investigating …

In this paper, we propose a novel outlier detection model to find outliers that deviate from the
generating mechanisms of normal instances by considering combinations of different …