Overview

This repository hosts fast parallel DBSCAN clustering code for lower dimensional Euclidean space. The code automatically uses all available POSIX threads to speedup DBSCAN clustering. It stems from a paper presented in SIGMOD'20: Theoretically Efficient and Practical Parallel DBSCAN.

Our software on 1 thread is on par with all serial state-of-the-art DBSCAN packages, and provides additional speedup via multi-threading. Below, we show a simple benchmark comparing our code with the DBSCAN implementation of Sklearn, tested on a 4-core computer, and a visualization of the clustering result. The time saved will be more significant on a larger data set and a machine with more cores.

Currently the software supports data sets with dimensionality 2 - 20.

Tutorial

Option 1: Use the binary executable

Compile and run the program:

cd executable
mkdir build
cd build
cmake ..
make -j # this will take a while
./dbscan -eps 0.1 -minpts 10 -o clusters.txt <data-file>

The <data-file> can be any CSV-like point data with or without header, see an example here. The cluster output clusters.txt will contain a cluster ID on each line (other than the first-line header), giving a cluster assignment in the same ordering as the input file. A noise point will have a cluster ID of -1.

Option 2: Use the Python binding (experimental)

We are developing a Python wrapper, currently using Cython. Right now it is still experimental, as it is only supported on Ubuntu on x86_64 with Python 3.8+ (it is tested to work directly on a fresh copy of Ubuntu 20.04). There are two ways to install it:

• Install it using PyPI: pip3 install --user dbscan (the latest verion is 0.0.9)
• OR Compile it yourself: First install dependencies pip3 install -r src/requirements.txt and sudo apt install libpython3-dev. Navigate to src/, and run the ''make'' script ./make.sh, The compilation will take a few minutes, and generate a ''.so'' library containing the ''DBSCAN'' module.

An example is provided in 'src/example.py'. If the dependencies above are installed, simply run 'python3 src/example.py' to reproduce the plots above.

Python API

from dbscan import DBSCAN
labels, core_samples_mask = DBSCAN(X, eps=0.3, min_samples=10)

Input

• X: A 2-D Numpy array (dtype=np.float64) containing the input data points. The first dimension of X is the number of data points n, and the second dimension is the data set dimensionality (the maximum supported dimensionality is 20).
• eps: The epsilon parameter (default 0.5).
• min_samples: The minPts parameter (default 5).

Output

• labels: A length n Numpy array (dtype=np.int32) containing cluster IDs of the data points, in the same ordering as the input data. Noise points are given a pseudo-ID of -1.
• core_samples_mask: A length n Numpy array (dtype=np.bool) masking the core points, in the same ordering as the input data.

We provide a complete example below that generates a toy data set, computes the DBSCAN clustering, and visualizes the result as shown in the plot above. Before running the example, first install packages for generating the data set and visualizing the result pip3 install --user sklearn matplotlib.

import numpy as np
from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler

# #############################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
                            random_state=0)
X = StandardScaler().fit_transform(X)

# #############################################################################
# Compute DBSCAN
from dbscan import DBSCAN
labels, core_samples_mask = DBSCAN(X, eps=0.3, min_samples=10)

# #############################################################################
# Plot result
import matplotlib.pyplot as plt

n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)
unique_labels = set(labels)
colors = [plt.cm.Spectral(each)
          for each in np.linspace(0, 1, len(unique_labels))]

for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = [0, 0, 0, 1]
    class_member_mask = (labels == k)
    xy = X[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=14)
    xy = X[class_member_mask & ~core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=6)

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

Help and Support

Please feel free to contact the developers or the paper authors if you encounter any problems, we are happy to patch/fix the program.

Citation

If you use our work in a publication, we would appreciate citations:

@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}
}