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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2021
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| Materias: | |
| 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 |