Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

Outlier detection has been used for centuries to detect and, where appropriate, remove
anomalous observations from data. Outliers arise due to mechanical faults, changes in …

The collection of digital information by governments, corporations, and individuals has
created tremendous opportunities for knowledge-and information-based decision making …

Most statistical and machine-learning algorithms assume that the data is a random sample
drawn from a stationary distribution. Unfortunately, most of the large databases available for …

Fraud is increasing dramatically with the expansion of modern technology and the global
superhighways of communication, resulting in the loss of billions of dollars worldwide each …

Web usage mining is the application of data mining techniques to discover usage patterns
from Web data, in order to understand and better serve the needs of Web-based …

In real-world environments it usually is difficult to specify target operating conditions
precisely, for example, target misclassification costs. This uncertainty makes building robust …