Outlier detection integrating semantic knowledge

Abstract

Existing proposals on outlier detection didn’t take the semantic knowledge of the dataset into consideration. They only tried to find outliers from dataset itself, which prevents finding more meaningful outliers. In this paper, we consider the problem of outlier detection integrating semantic knowledge. We introduce new definition for outlier: semantic outlier. A semantic outlier is a data point, which behaves differently with other data points in the same class. A measure for identifying the degree of each object being an outlier is presented, which is called semantic outlier factor (SOF). An efficient algorithm for mining semantic outliers based on SOF is also proposed. Experimental results show that meaningful and interesting outliers can be found with our method.