ORCA: Outlier detection and Robust Clustering for Attributed graphs
Here, 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 meth...
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Online Access: | http://www.osti.gov/servlets/purl/1890351 https://www.osti.gov/biblio/1890351 https://doi.org/10.1007/s10898-021-01024-z |
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ftosti:oai:osti.gov:1890351 2023-07-30T04:06:08+02:00 ORCA: Outlier detection and Robust Clustering for Attributed graphs Eswar, Srinivas Kannan, Ramakrishnan Vuduc, Richard Park, Haesun 2022-11-07 application/pdf http://www.osti.gov/servlets/purl/1890351 https://www.osti.gov/biblio/1890351 https://doi.org/10.1007/s10898-021-01024-z unknown http://www.osti.gov/servlets/purl/1890351 https://www.osti.gov/biblio/1890351 https://doi.org/10.1007/s10898-021-01024-z doi:10.1007/s10898-021-01024-z 97 MATHEMATICS AND COMPUTING 2022 ftosti https://doi.org/10.1007/s10898-021-01024-z 2023-07-11T10:15:17Z Here, 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. Other/Unknown Material Orca SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Journal of Global Optimization 81 4 967 989 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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unknown |
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97 MATHEMATICS AND COMPUTING |
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97 MATHEMATICS AND COMPUTING Eswar, Srinivas Kannan, Ramakrishnan Vuduc, Richard Park, Haesun ORCA: Outlier detection and Robust Clustering for Attributed graphs |
topic_facet |
97 MATHEMATICS AND COMPUTING |
description |
Here, 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. |
author |
Eswar, Srinivas Kannan, Ramakrishnan Vuduc, Richard Park, Haesun |
author_facet |
Eswar, Srinivas Kannan, Ramakrishnan Vuduc, Richard Park, Haesun |
author_sort |
Eswar, Srinivas |
title |
ORCA: Outlier detection and Robust Clustering for Attributed graphs |
title_short |
ORCA: Outlier detection and Robust Clustering for Attributed graphs |
title_full |
ORCA: Outlier detection and Robust Clustering for Attributed graphs |
title_fullStr |
ORCA: Outlier detection and Robust Clustering for Attributed graphs |
title_full_unstemmed |
ORCA: Outlier detection and Robust Clustering for Attributed graphs |
title_sort |
orca: outlier detection and robust clustering for attributed graphs |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1890351 https://www.osti.gov/biblio/1890351 https://doi.org/10.1007/s10898-021-01024-z |
genre |
Orca |
genre_facet |
Orca |
op_relation |
http://www.osti.gov/servlets/purl/1890351 https://www.osti.gov/biblio/1890351 https://doi.org/10.1007/s10898-021-01024-z doi:10.1007/s10898-021-01024-z |
op_doi |
https://doi.org/10.1007/s10898-021-01024-z |
container_title |
Journal of Global Optimization |
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81 |
container_issue |
4 |
container_start_page |
967 |
op_container_end_page |
989 |
_version_ |
1772818561270546432 |