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 …

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 …

Anomaly detection, which aims to identify observations that deviate from a nominal sample,
is a challenging task for high‐dimensional data. Traditional distance‐based anomaly …

Presence of outliers critically affects many pattern classification tasks. In this paper, we
propose a novel dynamic outlier detection method based on neighborhood rank difference …

Innovators Marketplace, a market-like workshop where cards showing existing pieces of
knowledge in various domains are combined to create ideas of services/products and …

Outliers and outlier detection are among the most important concepts of data processing in
different applications. While there are many methods for outlier detection, each detection …

Outlier detection in the medical and public health domains typically works with patient
records and is a very critical problem. This paper elaborates how the outliers can be …

(57) ABSTRACT A method for providing a personalized health risk of a patient includes
receiving training data corresponding to a plurality of patients and target data corresponding …