Minimum Average Cost Clustering
Abstract
A number of objective functions in clustering problems can be described with submodular functions. In this paper, we introduce the minimum average cost criterion, and show that the theory of intersecting submodular functions can be used for clustering with submodular objective functions. The proposed algorithm does not require the number of clusters in advance, and it will be determined by the property of a given set of data points. The minimum average cost clustering problem is parameterized with a real variable, and surprisingly, we show that all information about optimal clusterings for all parameters can be computed in polynomial time in total. Additionally, we evaluate the performance of the proposed algorithm through computational experiments.
Cite
Text
Nagano et al. "Minimum Average Cost Clustering." Neural Information Processing Systems, 2010.Markdown
[Nagano et al. "Minimum Average Cost Clustering." Neural Information Processing Systems, 2010.](https://mlanthology.org/neurips/2010/nagano2010neurips-minimum/)BibTeX
@inproceedings{nagano2010neurips-minimum,
title = {{Minimum Average Cost Clustering}},
author = {Nagano, Kiyohito and Kawahara, Yoshinobu and Iwata, Satoru},
booktitle = {Neural Information Processing Systems},
year = {2010},
pages = {1759-1767},
url = {https://mlanthology.org/neurips/2010/nagano2010neurips-minimum/}
}