Visible to the public Robust Contextual Outlier Detection: Where Context Meets Sparsity

TitleRobust Contextual Outlier Detection: Where Context Meets Sparsity
Publication TypeConference Paper
Year of Publication2016
AuthorsLiang, Jiongqian, Parthasarathy, Srinivasan
Conference NameProceedings of the 25th ACM International on Conference on Information and Knowledge Management
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4073-1
Keywordsbehavioral attributes, contextual attributes, Outlier detection, pubcrawl170201, scalable algorithm

Outlier detection is a fundamental data science task with applications ranging from data cleaning to network security. Recently, a new class of outlier detection algorithms has emerged, called contextual outlier detection, and has shown improved performance when studying anomalous behavior in a specific context. However, as we point out in this article, such approaches have limited applicability in situations where the context is sparse (i.e., lacking a suitable frame of reference). Moreover, approaches developed to date do not scale to large datasets. To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD). We utilize a local and global behavioral model based on the relevant contexts, which is then integrated in a natural and robust fashion. We run ROCOD on both synthetic and real-world datasets and demonstrate that it outperforms other competitive baselines on the axes of efficacy and efficiency. We also drill down and perform a fine-grained analysis to shed light on the rationale for the performance gains of ROCOD and reveal its effectiveness when handling objects with sparse contexts.

Citation Keyliang_robust_2016