An outlier detection algorithm based on an integrated outlier factor

Outlier detection is an important task in data mining. In this paper, a novel outlier detection algorithm is proposed, which integrates the local density with the global distance seamlessly. In the proposed method, an integrated outlier factor is used to measure the detecting accuracy. A comprehens ive experimental study on both synthetic and real-life datasets shows that the proposed method is more effective than some typical outlier detection methods, including Relative Density-based Outlier Score (RDOS), INFLuenced Outlierness (INFLO), Local Outlier Factor (LOF) and Local Distance-based Outlier detection Factor (LDOF).