Outlier Detection Technique in Data Mining: A Research Perspective

Abstract

While the field of data mining has been studied extensively, most of the work has concentrated on discovery of patterns. Outlier detection as a branch of data mining has many important applications, and deserves more attention from data mining community. Most methods in the early work that detects outliers independently have been developed in field of Statistics. Finding ,removing and detecting outliers is very important in data mining, for example error in large databases can be extremely common, so an important property of a data mining algorithm is robustness with respect to outliers in the database. 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. In this paper we will explain the first part of our research, which is focused on outlier identification and provide a description of why an identified outlier exceptional, based on Distance-Based outlier detection and Density-Based outlier detection.