• This repository provides implementations of Ex-DPC, Approx-DPC, and S-Approx-DPC.
• They are fast algorithms for density-peaks clustering (proposed in Science).
• As for the details about these algorithms, please read our SIGMOD2021 paper, Fast Density-Peaks Clustering: Multicore-based Parallelization Approach or arXiv one that corrects our theoretical analyses.
• [New] A faster algorithm is available here and dynamic cluster update is available here.
  • I recommend this new version.

• spatial library
  • We used version 2.1.8.
• Boost 1.67.0
  • We have not confirmed the availability of the other versions.
• The source codes of DPC algorithms have to be changed based on your paths of the above libraries.

• We assume low-dimensional datasets, as we use a kd-tree.
• Implementation is based on https://github.com/gishi523/kd-tree and spatial library.

• Ex-DPC
  • Compile: g++ -O3 main.cpp -o exdpc.out -fopenmp and run: ./exdpc.out.
• Approx-DPC
  • Compile: g++ -O3 main.cpp -o approxdpc.out -fopenmp and run: ./approxdpc.out.
• S-Approx-DPC
  • Compile: g++ -O3 main.cpp -o sapproxdpc.out -fopenmp and run: ./sapproxdpc.out.

• As an example, we have prepared a 2-dimensional synthetic dataset used in our paper.
• If you want to test your dataset,
  • Put the file at dataset directory.
  • Assign a unique dataset ID.
  • Set the dimensionality at data.hpp.
  • Write codes for inputing the data file in input_data() function of file_io.hpp.
  • Add a directory in result and update the function compute_direcotry().
  • Compile the code and run .out file.

• Set some value in the corresponding txt file in parameter.
• For \rho_min and \delta_min, we specify them in file_io.hpp.

• If you want to compute RI (rand index), ARI, or NMI,
  • get cluster labels from Ex-DPC, and
  • consult here.

If you use our implementation, please cite the following paper.

@inproceedings{amagata2021dpc,  
    title={Fast Density-Peaks Clustering: Multicore-based Parallelization Approach},  
    author={Amagata, Daichi and Hara, Takahiro},  
    booktitle={SIGMOD},  
    pages={49--61},  
    year={2021}  
}

Copyright (c) 2020 Daichi Amagata
This software is released under the MIT license.