Sesame

About

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

Quick Start

Installation

pip3 install pysame

Python Example

#!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))

Build Sesame

Prerequisites

• gcc 12 or lator
• cmake 3.14 or later
• boost
• gflags

Checkout Source Code

git clone https://github.com/DataSysResearch/Sesame --recursive --depth=1
cd Sesame

Build

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

Run Tests

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

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

Datasets can download from zenodo: 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/DataSysResearch/Sesame},
	doi	         = {10.1145/3589307},
    url          = {https://doi.org/10.1145/3589307}
}