https://github.com/d-chambers/dbscan1d

An efficient 1D implementation of the DBSCAN clustering algorithm
https://github.com/d-chambers/dbscan1d

clustering dbscan-algorithm machine-learning python

Last synced: 10 months ago
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An efficient 1D implementation of the DBSCAN clustering algorithm

• Host: GitHub
• URL: https://github.com/d-chambers/dbscan1d
• Owner: d-chambers
• License: lgpl-3.0
• Created: 2019-10-07T05:04:44.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-10-09T20:38:49.000Z (over 1 year ago)
• Last Synced: 2025-06-12T18:41:37.683Z (11 months ago)
• Topics: clustering, dbscan-algorithm, machine-learning, python
• Language: Python
• Size: 146 KB
• Stars: 24
• Watchers: 2
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# DBSCAN1D

[![Coverage](https://codecov.io/gh/d-chambers/dbscan1d/branch/master/graph/badge.svg)](https://codecov.io/gh/d-chambers/dbscan1d)

[![Supported Versions](https://img.shields.io/pypi/pyversions/dbscan1d.svg)](https://pypi.python.org/pypi/dbscan1d)

[![PyPI](https://pepy.tech/badge/dbscan1d)](https://pepy.tech/project/dbscan1d)

[![Licence](https://www.gnu.org/graphics/lgplv3-88x31.png)](https://www.gnu.org/licenses/lgpl.html)

dbscan1d is a 1D implementation of the [DBSCAN algorithm](https://en.wikipedia.org/wiki/DBSCAN). It was created to efficiently

preform clustering on large 1D arrays.

[Sci-kit Learn's DBSCAN implementation](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) does

not have a special case for 1D, where calculating the full distance matrix is wasteful. It is much better to simply sort

the input array and performing efficient bisects for finding closest points. Here are the results of running the simple

profile script included with the package. In every case DBSCAN1D is much faster than scikit learn's implementation.

![image](https://github.com/d-chambers/dbscan1d/raw/master/profile_results.png)

## Installation

Simply use pip to install dbscan1d:

```bash

pip install dbscan1d

```

It only requires numpy.

## Quickstart

dbscan1d is designed to be interchangable with sklearn's implementation in almost

all cases.

```python

from sklearn.datasets import make_blobs

from dbscan1d.core import DBSCAN1D

# make blobs to test clustering

X = make_blobs(1_000_000, centers=2, n_features=1)[0]

# init dbscan object

dbs = DBSCAN1D(eps=.5, min_samples=4)

# get labels for each point

labels = dbs.fit_predict(X)

# show core point indices

dbs.core_sample_indices_

# get values of core points

dbs.components_

```

## Notes

- dbscan1d can return different group numbers than sklearn for non-core points which are within

eps distances of core points for two separate groups. For example:

 `--C1--C1--P--C2--C2`

Here C1 and C2 are core points for group 1 and group 2, respectively. If P is within eps of both C1 and

C2, dbscan1d will assign it the same label as the core point that is closest. Sklearn doesn't always do this.