Best of Both: A Hybridized Centroid-Medoid Clustering Heuristic
Abstract
Although each iteration of the popular k Means clustering heuristic scales well to larger problem sizes, it often requires an unacceptably-high number of iterations to converge to a solution. This paper introduces an enhancement of k -Means in which local search is used to accelerate convergence without greatly increasing the average computational cost of the iterations. The local search involves a carefully-controlled number of swap operations resembling those of the more robust k -Medoids clustering heuristic. We show empirically that the proposed method improves convergence results when compared to standard k -Means.
Cite
Text
Grira and Houle. "Best of Both: A Hybridized Centroid-Medoid Clustering Heuristic." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273536Markdown
[Grira and Houle. "Best of Both: A Hybridized Centroid-Medoid Clustering Heuristic." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/grira2007icml-best/) doi:10.1145/1273496.1273536BibTeX
@inproceedings{grira2007icml-best,
title = {{Best of Both: A Hybridized Centroid-Medoid Clustering Heuristic}},
author = {Grira, Nizar and Houle, Michael E.},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {313-320},
doi = {10.1145/1273496.1273536},
url = {https://mlanthology.org/icml/2007/grira2007icml-best/}
}