Code to recreate the experiments from the paper ''Connecting the Dots: density-connectivity distance unifies DBSCAN, k-center and spectral clustering.''
The distance metric is calculated in distance_metric.py
. The k-center clustering on the dc-dist is given in density_tree.py
and cluster_tree.py
. We provide an
implementation of DBSCAN* in DBSCAN.py
. Furthermore, our implementation of Ultrametric Spectral Clustering is given in SpectralClustering.py
.
The code to calculate the distance measure can be found in distance_metric.py
. Experiment scripts are then located in
k_vs_epsilon.py
noise_robustness.py
distances_plot.py
compare_clustering.py
If you would like to mess around with the clusterings and assert for yourself that they are equivalent, we recommend the sandbox file cluster_dataset.py
.
We provide an ultrametric visualization tool in the file tree_plotting.py
. This allows you to look at the tree of dc-distances given by a specific dataset.
You will have to download the coil-100 dataset from here and unpack it
into the path data/coil-100
.
Feel free to email if you have any questions -- [email protected]