D-Cube: Dense-Block Detection in Terabyte-Scale Tensors

D-Cube (Disk-based Dense-block Detection) is an algorithm for detecting dense subtensors in tensors. D-Cube has the following properties:

• scalable: D-Cube handles large data not fitting in memory or even on a disk.
• fast: Even when data fit in memory, D-Cube outperforms its competitors in terms of speed.
• accurate: D-Cube detects dense subtensors in real-world tensors accurately, providing theoretical accuracy guarantees.

Datasets

The download links for the datasets used in the paper are here

Building and Running D-Cube

Please see User Guide

Running Demo

For demo, please type 'make'

Reference

If you use this code as part of any published research, please acknowledge the following paper.

@inproceedings{shin2017dcube,
  title        = {D-cube: Dense-block detection in terabyte-scale tensors},
  author       = {Shin, Kijung and Hooi, Bryan and Kim, Jisu and Faloutsos, Christos},
  booktitle    = {Proceedings of the Tenth ACM International Conference on Web Search and Data Mining},
  pages        = {681--689},
  year         = {2017},
  organization = {ACM}
}

@article{shin2021detecting,
  title     = {Detecting Group Anomalies in Tera-Scale Multi-Aspect Data via Dense-Subtensor Mining},
  author    = {Shin, Kijung and Hooi, Bryan and Kim, Jisu and Faloutsos, Christos},
  journal   = {Frontiers in Big Data},
  volume    = {3},      
  pages     = {58},
  year      = {2021},
  url       = {https://www.frontiersin.org/article/10.3389/fdata.2020.594302},       
  doi       = {10.3389/fdata.2020.594302},      
  issn      = {2624-909X}
}