Efficient Parameter-Free Clustering Using First Neighbor Relations
Abstract
We present a new clustering method in the form of a single clustering equation that is able to directly discover groupings in the data. The main proposition is that the first neighbor of each sample is all one needs to discover large chains and finding the groups in the data. In contrast to most existing clustering algorithms our method does not require any hyper-parameters, distance thresholds and/or the need to specify the number of clusters. The proposed algorithm belongs to the family of hierarchical agglomerative methods. The technique has a very low computational overhead, is easily scalable and applicable to large practical problems. Evaluation on well known datasets from different domains ranging between 1077 and 8.1 million samples shows substantial performance gains when compared to the existing clustering techniques.
Cite
Text
Sarfraz et al. "Efficient Parameter-Free Clustering Using First Neighbor Relations." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00914Markdown
[Sarfraz et al. "Efficient Parameter-Free Clustering Using First Neighbor Relations." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/sarfraz2019cvpr-efficient/) doi:10.1109/CVPR.2019.00914BibTeX
@inproceedings{sarfraz2019cvpr-efficient,
title = {{Efficient Parameter-Free Clustering Using First Neighbor Relations}},
author = {Sarfraz, Saquib and Sharma, Vivek and Stiefelhagen, Rainer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.00914},
url = {https://mlanthology.org/cvpr/2019/sarfraz2019cvpr-efficient/}
}