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ICTAI
2006
IEEE

Outlier Detection Using Random Walks

13 years 9 months ago
Outlier Detection Using Random Walks
The discovery of objects with exceptional behavior is an important challenge from a knowledge discovery standpoint and has attracted much attention recently. In this paper, we present a stochastic graph-based algorithm, called OutRank, for detecting outlying objects. In our method, a matrix is constructed using the similarity between objects and used as the adjacency matrix of the graph representation. The heart of this approach is the Markov 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 powerful 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 a lower false alarm rate are achieved using our proposed framework.
H. D. K. Moonesinghe, Pang-Ning Tan
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICTAI
Authors H. D. K. Moonesinghe, Pang-Ning Tan
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