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 run the tests contained in the Jupyter notebook, you will need to install the Jupyter matlab kernel.
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.
mpm install knn-graphs mpm install matlab-rnn-dbscan
- Download knn-graphs from the MATLAB File Exchange
- Download matlab-rnn-dbscan from the MATLAB File Exchange or from the latest GitHub release
- Add both packages to your MATLAB path
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);
rnndbscan.cluster(); % Or cluster(rnndbscan); % Inspect clusters, outliers, and labels rnndbscan.Clusters rnndbscan.Outliers rnndbscan.Labels
For more details, see the help text:
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.
Trevor Vannoy (2023). RNN DBSCAN (https://github.com/tvannoy/matlab-rnn-dbscan/releases/tag/v1.0.2), GitHub. Retrieved .
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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