Stream clustering algorithms on modern hardware
Project description
Sesame
This project aims at building a scalable stream mining library on modern hardware.
- The repo contains currently several representative real-world stream clustering algorithms and several synthetic algorithms.
- We welcome your contributions, if you are interested to contribute to the project, please fork and submit a PR. If you have questions, feel free to log an issue.
Build Dependency
Real-world algorithms
Algorithm | Window Model | Outlier Detection | Summarizing Data Structure | Offline Refinement |
---|---|---|---|---|
BIRCH | LandmarkWM | OutlierD | CFT | ❌ |
CluStream | LandmarkWM | OutlierD-T | MCs | ✅ |
DenStream | DampedWM | OutlierD-BT | MCs | ✅ |
DStream | DampedWM | OutlierD-T | Grids | ❌ |
StreamKM++ | LandmarkWM | NoOutlierD | CoreT | ✅ |
DBStream | DampedWM | OutlierD-T | MCs | ✅ |
EDMStream | DampedWM | OutlierD-BT | DPT | ❌ |
SL-KMeans | SlidingWM | NoOutlierD | AMS | ❌ |
Synthetic algorithms
Algorithm | Window Model | Outlier Detection | Summarizing Data Structure | Offline Refinement |
---|---|---|---|---|
G1 | LandmarkWM | OutlierD | MCs | ✅ |
G2 | LandmarkWM | OutlierD | MCs | ✅ |
G3 | LandmarkWM | OutlierD | CFT | ❌ |
G4 | SlidingWM | OutlierD | MCs | ❌ |
G5 | DampedWM | OutlierD-B | MCs | ❌ |
G6 | LandmarkWM | NoOutlierD | MCs | ❌ |
G8 | LandmarkWM | OutlierD | MCs | ❌ |
G9 | LandmarkWM | OutlierD | Grids | ❌ |
G10 | LandmarkWM | OutlierD | DPT | ❌ |
G11 | LandmarkWM | OutlierD-T | MCs | ❌ |
G12 | LandmarkWM | OutlierD-B | MCs | ❌ |
G13 | LandmarkWM | OutlierD-BT | MCs | ❌ |
G14 | LandmarkWM | OutlierD | AMS | ❌ |
G15 | LandmarkWM | OutlierD | CoreT | ❌ |
Datasets
DataSet | Length | Dimension | Cluster Number |
---|---|---|---|
CoverType | 581012 | 54 | 7 |
KDD-99 | 4898431 | 41 | 23 |
Insects | 905145 | 33 | 24 |
Sensor | 2219803 | 5 | 55 |
EDS | 45690, 100270, 150645, 200060, 245270 | 2 | 75, 145, 218, 289, 363 |
ODS | 94720,97360,100000 | 2 | 90, 90, 90 |
You may download the datasets here: https://zenodo.org/records/8210331
How to Cite Sesame
- [SIGMOD 2023] Xin Wang and Zhengru Wang and Zhenyu Wu and Shuhao Zhang and Xuanhua Shi and Li Lu. Data Stream Clustering: An In-depth Empirical Study, SIGMOD, 2023
@inproceedings{wang2023sesame,
title = {Data Stream Clustering: An In-depth Empirical Study},
author = {Xin Wang and Zhengru Wang and Zhenyu Wu and Shuhao Zhang and Xuanhua Shi and Li Lu},
year = 2023,
booktitle = {Proceedings of the 2023 International Conference on Management of Data (SIGMOD)},
location = {Seattle, WA, USA},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {SIGMOD '23},
abbr = {SIGMOD},
bibtex_show = {true},
selected = {true},
pdf = {papers/Sesame.pdf},
code = {https://github.com/intellistream/Sesame},
doi = {10.1145/3589307},
url = {https://doi.org/10.1145/3589307}
}
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