LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Outlier Detection Using Random Walks
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
Discovering Contexts and Contextual Outliers Using Random Walks in Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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Discovering STCOD is an important problem with many applications such as geological disaster monitoring, geophysical exploration, public safety and health etc. However, determining suitable interest measure thresholds is a difficult task. In the paper, we define the problem of mining at most top-K% STCOD patterns without using user-defined thresholds and propose a novel at most top-K% STCOD mining algorithm by using a graph based random walk model. Analytical and experimental results show that the proposed algorithm is correct and complete. Results show the proposed method is computationally more efficient than naive algorithms. The effectiveness of our methods is justified by empirical results on real data sets. It shows that the algorithms are effective and validate.