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 …

As an essential task in data mining, outlier detection identifies abnormal patterns in
numerous applications, among which clustering-based outlier detection is one of the most …

Outlier detection is an important topic in machine learning and has been used in a wide
range of applications. In this paper, we approach outlier detection as a binary-classification …

The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG),
widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also …

As an important technique for data pre-processing, outlier detection plays a crucial role in
various real applications and has gained substantial attention, especially in medical fields …

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 …

With the advent of 5G, the industrial Internet of Things has developed rapidly. The industrial
sensor-cloud system (SCS) has also received widespread attention. In the future, a large …

In supervised learning it has been shown that a collection of weak classifiers can result in a
strong classifier with error rates similar to those of more sophisticated methods. In …

Ensembles for unsupervised outlier detection is an emerging topic that has been neglected
for a surprisingly long time (although there are reasons why this is more difficult than …