Semi-supervised Outlier Detection with only Positive and Unlabeled Data based on Fuzzy Clustering

The task of semi-supervised outlier detection is to find the instances that are exceptional from other data with the use of some labeled examples. In many applications such as fraud detection and intrusion detection, this issue becomes important. Most existing techniques are unsupervised and the semi-supervised approaches use both negative and positive instances to detect outliers. However, in many real-world applications, very few positive labeled examples are available. This paper proposes an effective method to address this problem. This method is based on two steps. First, extracting reliable negative instances by KNN technique and then using fuzzy clustering with both negative and positive examples for outlier detection. Experimental results on real datasets demonstrate that the proposed method outperforms the previous methods in detecting outliers.