dbsod 0.0.5

DBSOD: Density-Based Spatial Outlier Detection

DBSOD: Density-Based Spatial Outlier Detection

Official implementation of "DBSOD: Density-Based Spatial Outlier Detection". Paper preprint is coming soon.

Algorithm

While DBSCAN is a widely used clustering algorithm, it only provides a binary label for outliers and does not assign a continuous outlierness score. To address this limitation, we propose DBSOD, a density-based spatial outlier detection method inspired by DBSCAN. The algorithm estimates the consistency with which a data point is identified as an outlier across a range of neighborhood sizes:

The algorithm systematically varies the neighborhood size parameter $\epsilon$, evaluating outlierness across multiple density assumptions. By aggregating binary outlier classifications across these scales, it produces a normalized outlierness score for each point, reflecting how consistently the point is identified as an outlier.

Installation

Note: the package was developed for Linux (manylinux, x86_64) machines.

You can install package using pip:

pip install dbsod

Alternatively (for instance if you want to contribute) you may clone this repository, build dbsod and install it in .venv in editable mode:

git clone https://github.com/Kowd-PauUh/dbsod.git
cd dbsod
make install_g++
make install_eigen
make venv

Usage

Take as an example this dataset:

import numpy as np

DATA = np.array([
    [0.35, 0.18],
    [0.60, 0.16],
    [0.40, 0.18],
    [0.40, 0.30],
    [0.30, 0.70],
])

We can use dbsod to calculate outlierness score for each point:

from dbsod import dbsod

EPS_SPACE = [0.15, 0.22]  # `eps` parameters used for calculating normalized outlierness score
MIN_PTS = 2               # minimum number of neighbors for the data point to become "core" point

# compute outlierness scores
outlierness_scores = dbsod(
    X=DATA,
    eps_space=EPS_SPACE,
    min_pts=MIN_PTS,
    metric='euclidean'
)

print(outlierness_scores)

The output will be: array([0. , 0.5, 0. , 0. , 1. ]).

Below is the visualization of this example:

On the real-world data (check out this example) result of DBSOD would look like:

Citation

@software{danylenko2025dbsod, 
  author = {Danylenko, Ivan},
  doi = {10.5281/zenodo.17806900},
  month = jul,
  title = {{DBSOD: Density-Based Spatial Outlier Detection}},
  url = {https://github.com/Kowd-PauUh/dbsod},
  version = {0.0.5},
  year = {2025}
}