finch-clust 0.2.3

FINCH - First Integer Neighbor Clustering Hierarchy: A parameter-free fast clustering algorithm.

First Integer Neighbor Clustering Hierarchy (FINCH) Algorithm

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.

Installation

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]"

Usage:

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

A minimal correponding Matlab implementation is provided in the matlab directory.

Demos

The following demo notebooks are available to see the usage in clustering a dataset.

1. Basic usage on 2D toy data
2. Clustering STL-10 dataset with FINCH

Relevant tools built on FINCH

• h-nne: See also our h-nne method which uses FINCH for fast dimenionality reduction and visualization applications.

• TW-FINCH: Also see our TW-FINCH variant which is useful for video segmentation.

Citation

@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}
}