A Spectral Clustering Based Outlier Detection Technique

Outlier detection shows its increasingly high practical value in many application areas such as intrusion detection, fraud detection, discovery of criminal activities in electronic commerce and so on. Many techniques have been developed for outlier detection, including distribution-based outlier detection algorithm, depth-based outlier detection algorithm, distance-based outlier detection algorithm, density-based outlier detection algorithm and clustering-based outlier detection. Spectral clustering receives much attention as a competitive clustering algorithms emerging in recent years. However, it is not very well scalable to modern large datasets. To partially circumvent this drawback, in this paper, we propose a new outlier detection method inspired by spectral clustering. Our algorithm combines the concept of kNN and spectral clustering techniques to obtain the abnormal data as outliers by using the information of eigenvalues and eigenvectors statistically in the feature space. We compare the performance of our methods with distance-based outlier detection methods and density-based outlier detection methods. Experimental results show the effectiveness of our algorithm for identifying outliers.