FINCH is a parameter-free fast and scalable clustering algorithm. it stands out for its speed and clustering quality. The algorithm is described in our paper Efficient Parameter-free Clustering Using First Neighbor Relations published in CVPR 2019 . Read Paper.
The project is available in PyPI. To install run:
pip install finch-clust
Optional. Install PyNNDescent to get first neighbours for large data
To install finch with pynndescent run:
pip install "finch-clust[ann]"
typically you would run:
from finch import FINCH
c, num_clust, req_c = FINCH(data)
You can set options e.g., required number of cluster or distance etc,
c, num_clust, req_c = FINCH(data, initial_rank=None, req_clust=None, distance='cosine', verbose=True)
For more details on meaning of input arguments check README in finch directory.
Matlab usage
Correponding Matlab implementation is provided in the matlab directory.
The following demo notebooks are available to see the usage in clustering a dataset.
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h-nne: See also our h-nne method which uses FINCH for fast dimenionality reduction and visualization applications.
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TW-FINCH: Also see our TW-FINCH variant which is useful for video segmentation.
@inproceedings{finch,
author = {M. Saquib Sarfraz and Vivek Sharma and Rainer Stiefelhagen},
title = {Efficient Parameter-free Clustering Using First Neighbor Relations},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {8934--8943}
year = {2019}
}