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 …

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 …

Sophisticated online banking fraud reflects the integrative abuse of resources in social,
cyber and physical worlds. Its detection is a typical use case of the broad-based Wisdom …

Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide
each year, several modern techniques in detecting fraud are continually developed and …

As deregulation, new technologies, and new competitors open up the mobile
telecommunications industry, churn prediction and management has become of great …

Outliers once upon a time regarded as noisy data in statistics, has turned out to be an
important problem which is being researched in diverse fields of research and application …

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 …

We propose a fraud detection method based on the user accounts visualization and
threshold-type detection. The visualization technique employed in our approach is the Self …