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ORCA: Outlier detection and Robust Clustering for Attributed graphs

Author

Listed:
  • Srinivas Eswar

    (Georgia Institute of Technology)

  • Ramakrishnan Kannan

    (Oak Ridge National Laboratory)

  • Richard Vuduc

    (Georgia Institute of Technology)

  • Haesun Park

    (Georgia Institute of Technology)

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.

Suggested Citation

  • Srinivas Eswar & Ramakrishnan Kannan & Richard Vuduc & Haesun Park, 2021. "ORCA: Outlier detection and Robust Clustering for Attributed graphs," Journal of Global Optimization, Springer, vol. 81(4), pages 967-989, December.
    • Handle: RePEc:spr:jglopt:v:81:y:2021:i:4:d:10.1007_s10898-021-01024-z
    DOI: 10.1007/s10898-021-01024-z
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    References listed on IDEAS

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    3. Jingu Kim & Yunlong He & Haesun Park, 2014. "Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework," Journal of Global Optimization, Springer, vol. 58(2), pages 285-319, February.
    4. Da Kuang & Sangwoon Yun & Haesun Park, 2015. "SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering," Journal of Global Optimization, Springer, vol. 62(3), pages 545-574, July.
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