Nearest Neighbor Median Shift Clustering for Binary Data
Machine Learning
2022-03-04 v1 Artificial Intelligence
Machine Learning
Abstract
We describe in this paper the theory and practice behind a new modal clustering method for binary data. Our approach (BinNNMS) is based on the nearest neighbor median shift. The median shift is an extension of the well-known mean shift, which was designed for continuous data, to handle binary data. We demonstrate that BinNNMS can discover accurately the location of clusters in binary data with theoretical and experimental analyses.
Keywords
Cite
@article{arxiv.1902.04181,
title = {Nearest Neighbor Median Shift Clustering for Binary Data},
author = {Gaël Beck and Tarn Duong and Mustapha Lebbah and Hanane Azzag},
journal= {arXiv preprint arXiv:1902.04181},
year = {2022}
}
Comments
Algorithms are available at https://github.com/Clustering4Ever/Clustering4Ever