Detecting Group Anomalies in Tera-Scale Multi-Aspect Data via Dense-Subtensor Mining

How can we detect fraudulent lockstep behavior in large-scale multi-aspect data (i.e., tensors)? Can we detect it when data are too large to fit in memory or even on a disk? Past studies have shown that dense subtensors in real-world tensors (e.g., social media, Wikipedia, TCP dumps, etc.) signal an...

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Detalles Bibliográficos
Autores principales: Shin, Kijung, Hooi, Bryan, Kim, Jisu, Faloutsos, Christos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118605/
https://www.ncbi.nlm.nih.gov/pubmed/33997776
http://dx.doi.org/10.3389/fdata.2020.594302

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118605/
https://www.ncbi.nlm.nih.gov/pubmed/33997776
http://dx.doi.org/10.3389/fdata.2020.594302

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