In spite of many statistical methods for outlier detection and for robust analysis, there is little
work on further analysis of outliers themselves to determine their origins. For example, there …

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 …

Outliers are difficult to handle because some of them can be measurement errors, while
others may represent phenomena of interest, something" significant" from the viewpoint of …

Nowadays, outlier detection is primarily studied as an independent knowledge discovery
process merely because outliers might be indicators of interesting events that have never …

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 …

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 handling of anomalous or outlying observations in a data set is one of the most
important tasks in data pre-processing. It is important for three reasons. First, outlying …

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 …

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 …

Outlier detection is an extremely important problem with direct application in a wide variety
of domains. A key challenge with outlier detection is that it is not a wellformulated problem …