PDSC

About

PDSC is a C++ stream clustering benchmark for educational purposes

Build Sesame

Prerequisites

• gcc 11 or lator
• cmake 3.17 or later

Checkout Source Code

git clone https://github.com/intellistream/PDSC
cd PDSC

Build

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

Run Benchmark

Download datasets from Releases:

wget https://github.com/intellistream/PDSC/releases/download/dataset/covtype.csv

Run the benchmark:

./pdsc /path/to/{dataset}.csv [-n num_points]

Datasets

DataSet	Length	Dimensions	Cluster Number
CoverType	581012	54	7

Baseline Performance

Algorithm	Execution Time	Purity (correctness check)	Num of Clusters (correctness check)
BIRCH	1081 ms	0.0817814	57
CluStream	1001 ms	0.717314	7
EDMStream	181 ms	0.893519	14
DStream	778 ms	0.770046	101
DenStream	1045 ms	0.374782	15
SL-KMeans	3082507 ms	0.171415	7

Baseline performance is tested on CoverType dataset with 581012 points.

• CPU: AMD Ryzen 7 5700U
• RAM: 24GB DDR4 3200MHz

Reference

• [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}
}