README.txt

=================================================================================

 Detecting Group Anomalies in Tera-Scale Multi-Aspect Data via Dense-Subtensor Mining
 Authors: Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos

 Version: 2.0
 Date: August 13, 2020
 Main Contact: Kijung Shin (kijungs@kaist.ac.kr)

 This software is free of charge under research purposes.
 For commercial purposes, please contact the author.

 =================================================================================

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

For detailed information, see 'user_guide.pdf'

For demo, type 'make'