Metadata-Version: 2.1
Name: dbscan
Version: 0.0.5
Summary: Theoretically efficient and practical parallel DBSCAN
Home-page: https://github.com/wangyiqiu/dbscan-python
Author: Yiqiu Wang
Author-email: yiqiu_wang@icloud.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# Overview

This repository contains the fastest parallel code for Euclidean DBSCAN on low to moderate dimensional data sets. It stems from a SIGMOD'20 paper: [Theoretically Efficient and Practical Parallel DBSCAN](https://arxiv.org/abs/1912.06255).

# Citation

    @inproceedings{wang2020theoretically,
      author = {Wang, Yiqiu and Gu, Yan and Shun, Julian},
      title = {Theoretically-Efficient and Practical Parallel DBSCAN},
      year = {2020},
      isbn = {9781450367356},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3318464.3380582},
      doi = {10.1145/3318464.3380582},
      booktitle = {Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data},
      pages = {2555–2571},
      numpages = {17},
      keywords = {parallel algorithms, spatial clustering, DBScan},
      location = {Portland, OR, USA},
      series = {SIGMOD ’20}
    }


