The outlier detection problem has important applications in the fields of fraud detection, weather prediction, customer segmentationl and intrusion detection. Many recent algorithms use concepts of proximity in order to find outliers based on their relationship to the rest of the data. In this paper we proposed a new algorithm to detect outlier in high dimensional domains with mixed attributes based on clustering, and proposed a new method to measure similarity and outlyingness of objects. The algorithm we proposed can give near linear performance. The experimental results on KDDCUP99 and Wisc...
