Abstract
In recent years, many new techniques have been developed for mining and managing uncertain data. This is because of the new ways of collecting data which has resulted in enormous amounts of inconsistent or missing data. Such data is often remodeled in the form of uncertain data. In this paper, we will examine the problem of outlier detection with uncertain data sets. The outlier detection problem is particularly challenging for the uncertain case, because the outlier-like behavior of a data point may be a result of the uncertainty added to the data point. Furthermore, the uncertainty added to the other data points may skew the overall data distribution in such a way that true outliers may be masked. Therefore, it is critical to be able to remove the effects of the uncertainty added both at the aggregate level as well as at the level of individual data points. In this paper, we will examine a density based approach to outlier detection, and show how to use it to remove the uncertainty from the underlying data. We present experimental results illustrating the effectiveness of the method. Copyright © by SIAM.