ORCA: Outlier detection and Robust Clustering for Attributed graphs

Srinivas Eswar (), Ramakrishnan Kannan (), Richard Vuduc () and Haesun Park ()
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Srinivas Eswar: Georgia Institute of Technology
Ramakrishnan Kannan: Oak Ridge National Laboratory
Richard Vuduc: Georgia Institute of Technology
Haesun Park: Georgia Institute of Technology

Journal of Global Optimization, 2021, vol. 81, issue 4, No 6, 967-989

Abstract: Abstract A framework is proposed to simultaneously cluster objects and detect anomalies in attributed graph data. Our objective function along with the carefully constructed constraints promotes interpretability of both the clustering and anomaly detection components, as well as scalability of our method. In addition, we developed an algorithm called Outlier detection and Robust Clustering for Attributed graphs (ORCA) within this framework. ORCA is fast and convergent under mild conditions, produces high quality clustering results, and discovers anomalies that can be mapped back naturally to the features of the input data. The efficacy and efficiency of ORCA is demonstrated on real world datasets against multiple state-of-the-art techniques.

Keywords: Attributed graphs; Robust clustering; Anomaly detection; Joint matrix low rank approximation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10898-021-01024-z

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