Different Outlier Detection Algorithms in Data Mining: A Review

Outlier is defined as an observation that deviates too much from other observations. The identification of outliers can lead to the discovery of useful and meaningful knowledge. Outlier detection has been extensively studied in the past decades. However, most existing research focuses on the algorithm based on special background, compared with outlier detection approach is still rare. Most sophisticated methods in data mining address this problem to some extent, but not fully, and can be improved by addressing the problem more directly. The identification of outliers can lead to the discovery of unexpected knowledge in areas such as credit card fraud detection, calling card fraud detection, discovering criminal behaviors, discovering computer intrusion, etc. This paper mainly discusses and compares approach of different outlier detection from data mining perspective, which can be grouped into statistical-based approach, distance-based approach, density-based approach, Information theoretic-based approach.