Local Outlier Detecting Algorithm Based on WSRFCM Clustering

To reduce the amount of calculation for local outlier factor, on the basis of the LDOF algorithm, this paper proposed a novel outlier detect algorithm WSRFCM-LDOF. The algorithm adopted the integration of rough set and shadowed set into feature weighted fuzzy clustering, as a method of reducing the computational effort of local outliers. Associating feature with weights for each cluster is a common approach in clustering algorithms, and it can handle the different distribution of clusters effectively. The experimental results show the proposed algorithm has reduced the time complexity, meanwhile has improved the accuracy of detecting outliers.