文件列表:

LICENSE (1483, 2021-08-20)
RNN-DBSCAN tests.ipynb (2002755, 2021-08-20)
RnnDbscan.m (18467, 2021-08-20)
create_synthetic_data.py (2194, 2021-08-20)

# matlab-rnn-dbscan [](https://www.mathworks.com/matlabcentral/fileexchange/97924-rnn-dbscan) This repo contains a MATLAB implementation of the [RNN-DBSCAN algorithm by Bryant and Cios](https://doi.org/10.1109/TKDE.2017.2787***0). 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](https://doi.org/10.1109/TKDE.2017.2787***0). ## Dependencies - My [knn-graphs](https://github.com/tvannoy/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](https://github.com/calysto/matlab_kernel). To use the NN Descent algorithm to construct the KNN graph used by RNN DBSCAN, you need [pynndescent](https://github.com/lmcinnes/pynndescent) and [MATLAB's Python language interface](https://www.mathworks.com/help/matlab/call-python-libraries.html). I recommend using Conda to set up an environment, as MATLAB is picky about which Python versions it supports. ## Installation ### Install with [mpm](https://github.com/mobeets/mpm): ``` mpm install knn-graphs mpm install matlab-rnn-dbscan ``` ### Manual installation - Download [knn-graphs](https://www.mathworks.com/matlabcentral/fileexchange/97919-knn-graphs) from the MATLAB File Exchange - Download matlab-rnn-dbscan from the [MATLAB File Exchange](https://www.mathworks.com/matlabcentral/fileexchange/97924-rnn-dbscan) or from the latest [GitHub release](https://github.com/tvannoy/matlab-rnn-dbscan/archive/refs/tags/v1.0.1.zip) - Add both packages to your MATLAB path ## Usage `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: ```matlab % 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); ``` Clustering: ```matlab rnndbscan.cluster(); % Or cluster(rnndbscan); % Inspect clusters, outliers, and labels rnndbscan.Clusters rnndbscan.Outliers rnndbscan.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. ## Contributing All contributions are welcome! Just submit a pull request or open an issue.

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