MATLAB implementation of the RNN-DBSCAN clustering algorithm
Updated 7 Dec 2023


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This repo contains a MATLAB implementation of the RNN-DBSCAN algorithm by Bryant and Cios. This implementation is based upon the graph-based interpretation presented in their paper.

RNN DBSCAN is a density-based clustering algorithm that uses reverse nearest neighbor counts as an estimate of observation density. It is based upon traversals of the directed k-nearest neighbor graph, and can handle clusters of different densities, unlike DBSCAN. For more details about the algorithm, see the original paper.


  • My knn-graphs MATLAB library
  • Statistics and Machine Learning Toolbox

To use the NN Descent algorithm to construct the KNN graph used by RNN DBSCAN, you need pynndescent and MATLAB's Python language interface. I recommend using Conda to set up an environment, as MATLAB is picky about which Python versions it supports.


Install with mpm:

mpm install knn-graphs
mpm install matlab-rnn-dbscan

Manual installation


RnnDbscan is a class with a single public method, cluster. The results of the clustering operation are stored in read-only public properties.

Creating an RnnDbscan object:

% Create an RnnDbscan object using a 5-nearest-neighbor graph.
% nNeighborsIndex is how many neighbors used to create the knn index, and must be >= nNeighbors + 1
% because the index includes self-edges (each point is it's own nearest neighbor).
nNeighors = 5;
nNeighborsIndex = 6;
rnndbscan = RnnDbscan(data, nNeighbors, nNeighborsIndex);

% Use the NN Descent algorithm to create the knn index; this is much faster than an exhaustive search
rnndbscan = RnnDbscan(data, nNeighbors, nNeighborsIndex, 'Method', 'nndescent');

% Explicitly use an exhaustive search, which is the default
rnndbscan = RnnDbscan(data, nNeighbors, nNeighborsIndex, 'Method', 'knnsearch');

% Use a precomputed knn index
knnidx = knnindex(data, nNeighborsIndex);
rnndbscan = RnnDbscan(data, nNeighbors, knnidx);


% Or

% Inspect clusters, outliers, and labels

For more details, see the help text: help RnnDbscan. RNN-DBSCAN tests.ipynb also contains many tests, which can be used as usage examples.


All contributions are welcome! Just submit a pull request or open an issue.

Cite As

Trevor Vannoy (2024). RNN DBSCAN (https://github.com/tvannoy/matlab-rnn-dbscan/releases/tag/v1.0.4), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2020a
Compatible with R2020a and later releases
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

See release notes for this release on GitHub: https://github.com/tvannoy/matlab-rnn-dbscan/releases/tag/v1.0.4

See release notes for this release on GitHub: https://github.com/tvannoy/matlab-rnn-dbscan/releases/tag/v1.0.3

See release notes for this release on GitHub: https://github.com/tvannoy/matlab-rnn-dbscan/releases/tag/v1.0.2


See release notes for this release on GitHub: https://github.com/tvannoy/matlab-rnn-dbscan/releases/tag/v1.0.1


To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.