Abstract Aiming at the problem of incremental outlier detection with the dataset being updated, a density-neighbors-based incremental outlier detection algorithm is proposed. When the dataset is updated, the proposed algorithm identifies the affected objects and establishes the density neighbor sequences of the objects based on the change of the k-density of the object and those of its neighbors. According to the density neighbor sequence cost (DNSC) of the object and the average of the DNSC of k-distance neighbors of the object, the proposed algorithm calculates the incremental outlier factor(IOF) of each affected objects and the IOF value indicates the degree of the object as an outlier. Therefore, the proposed algorithm improves the effectiveness of incremental outlier detection. Moreover, it speeds up the outlier detection since the proposed algorithm recalculates the IOF values of these affected objects. The experimental results show that the proposed algorithm has a higher quality in outlier detection than the former incremental algorithms with the decrease of the running time.
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2009, Vol. 22 
