A project for scalable hierachical clustering, thanks to a Flexible, Incremental, Scalable, Hierarchical Density-Based Clustering algorithms (FISHDBC, for the friends).

This package lets you use an arbitrary dissimilarity function you write (or reuse from somebody else's work!) to cluster your data.

Please see the paper at https://arxiv.org/abs/1910.07283

• Python 3
• Cython
• hdbscan: https://github.com/scikit-learn-contrib/hdbscan
• scipy: https://www.scipy.org/

python3 setup.py install

A projects allowing scalable hierarchical clustering, thanks to an approximated version of OPTICS, on arbitrary data and distance measures.

Look at the HDBSCAN documentation for the meaning of the return values of the cluster method. There are plenty of configuration options, inherited by HNSWs and HDBSCAN, but the only compulsory argument is a dissimilarity function between arbitrary data elements:

import flexible_clustering

clusterer = flexible_clustering.FISHDBC(my_dissimilarity)
for elem in my_data:
    clusterer.add(elem)
labels, probs, stabilities, condensed_tree, slt, mst = clusterer.cluster()

for elem in some_new_data: # support cheap incremental clustering
    clusterer.add(elem)
# new clustering according to the newly available data
labels, probs, stabilities, condensed_tree, slt, mst = clusterer.cluster()

Make sure to run everything from outside the source directory, to avoid confusing Python path.

Look at the fishdbc_example.py file for something more (it requires matplotlib to be run).

Send me an email at [email protected]. I'll improve the docs as and if people use this.

Matteo Dell'Amico

BSD 3-clause; see the LICENSE file.