  Outlier detection is one of the most popular and continuously rising topics
in the data mining field due to its crucial academic value and extensive
industrial applications. Among different settings, unsupervised outlier
detection is the most challenging and practical one, which attracts tremendous
efforts from diverse perspectives. In this paper, we consider the score-based
outlier detection category and point out that the performance of current
outlier detection algorithms might be further boosted by score propagation.
Specifically, we propose Infinite Propagation of Outlier Factor (iPOF)
algorithm, an extremely and excitingly simple outlier detection booster via
infinite propagation. By employing score-based outlier detectors for
initialization, iPOF updates each data point's outlier score by averaging the
outlier factors of its nearest common neighbors. Extensive experimental results
on numerous datasets in various domains demonstrate the effectiveness and
efficiency of iPOF significantly over several classical and recent
state-of-the-art methods. We also provide the parameter analysis on the number
of neighbors, the unique parameter in iPOF, and different initial outlier
detectors for general validation. It is worthy to note that iPOF brings in
positive improvements ranging from 2% to 46% on the average level, and in some
cases, iPOF boosts the performance over 3000% over the original outlier
detection algorithm.
