A comprehensive survey of numeric and symbolic outlier mining techniques

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.