Journal
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Volume 17, Issue 1, Pages 19-36Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218213008003753
Keywords
outlier detection; random walk; Markov chain
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This paper introduces a stochastic graph-based algorithm, called OutRank, for detecting outliers in data. We consider two approaches for constructing a graph representation of the data, based on the object similarity and number of shared neighbors between objects. The heart of this approach is the Markov chain model that is built upon this graph, which assigns an outlier score to each object. Using this framework, we show that our algorithm is more robust than the existing outlier detection schemes and can effectively address the inherent problems of such schemes. Empirical studies conducted on both real and synthetic data sets show that significant improvements in detection rate and false alarm rate are achieved using the proposed framework.
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