Sesame is scalable stream mining library on modern hardware written in C++
By now Sesame contains several representative real-world stream clustering algorithms and synthetic algorithms
pip3 install pysame
#!python3
from pysame import Benne, Birch, BenneObj
X = [[0, 1], [0.3, 1], [-0.3, 1], [0, -1], [0.3, -1], [-0.3, -1]]
# run birch algorithm
brc = Birch(
n_clusters=2,
dim=2,
distance_threshold=0.5,
)
print(brc.partial_fit(X).predict(X))
# run benne algorithm
bne = Benne(
n_clusters=2,
dim=2,
distance_threshold=0.5,
obj=BenneObj.accuracy,
)
print(bne.partial_fit(X).predict(X))
git clone https://github.com/intellistream/Sesame --recursive --depth=1
cd Sesame
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)
Download the datasets from Zenodo and put them in the datasets
directory:
cd Sesame/datasets
pip3 install zenodo_get
zenodo_get 8210331
Run the tests:
cd Sesame/build/test
./google_test
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 | ❌ |
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 | ❌ |
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 |
Datasets can download from zenodo: https://zenodo.org/records/8210331
@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}
}