PARs: Predicate-based Association Rules for Efficient and Accurate Anomaly Explanation.
| Anthology ID: | DBLP:conf/cikm/000424 |
|---|---|
| Volume: | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 |
| Year: | 2024 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 612-621 |
| URL: | https://doi.org/10.1145/3627673.3679625 |
| DOI: | https://doi.org/10.1145/3627673.3679625 |
| DBLP: | conf/cikm/000424 |
| BibTeX: |
@inproceedings{feng-2024-pars,
author = {Cheng Feng},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{PARs: Predicate-based Association Rules for Efficient and Accurate Anomaly Explanation}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {612--621},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679625},
doi = {https://doi.org/10.1145/3627673.3679625}
}