Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given data set. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection, environment monitoring, etc. In this paper, we proposed a new definition of outlier, called cluster-based outlier. Comparing with the existing definitions, the cluster-based outlier is more suitable for the complicated data sets that consist of many clusters with different densities. To detect cluster-based outliers, we first split the given data set into a number of clusters using unsupervised extreme learning machines. Then, we further design a pruning method technique to efficiently compute outliers in each cluster. at last, the effectiveness and efficiency of the proposed approaches are verified through plenty of simulation experiments.