Abstract
Data that appear to have different characteristics than the rest of the population are called outliers. Identifying outliers from huge data repositories is a very complex task called outlier mining. Outlier mining has been akin to finding needles in a haystack. However, outlier mining has a number of practical applications in areas such as fraud detection, network intrusion detection, and identification of competitor and emerging business trends in e-commerce. This survey discuses practical applications of outlier mining, and provides a taxonomy for categorizing related mining techniques. A comprehensive review of these techniques with their advantages and disadvantages along with some current research issues are provided.
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