pysame 0.1.0

Stream clustering algorithms on modern hardware

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

• GCC-11 (In our paper, we use gcc-11.2.0)
• Boost: 1.78.0 Link
• GFLAGS: 2.2.0 Link

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}
}