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 -- draganovandrew@cs.au.dk