A Comparative Study on Nearest Neighbor Based Outlier Detection in Data Mining
Outlier detection is an important branch in the field of data mining, which is the discovery of data that deviate a lot from other data patterns. The identification of outlier is lead to the discovery of unexpected knowledge in the area such as fraud detection, intrusion detection, customer behavior analysis, fault diagnosis, time series monitoring, etc. This paper describes the performance of Nearest Neighbor based Techniques and Statistical based Approach for identifying outlier in data mining. Finally, the experimental results show that the both approaches of outlier identification techniques performances are compared.