ML Anthology

Using Ripley's K-Function to Improve Graph-Based Clustering Techniques

Kevin Streib, James W. Davis
CVPR 2011 pp. 2305-2312
doi:10.1109/CVPR.2011.5995509 /cvpr/2011/streib2011cvpr-using/

Abstract

The success of any graph-based clustering algorithm depends heavily on the quality of the similarity matrix being clustered, which is itself highly dependent on point-wise scaling parameters. We propose a novel technique for finding point-wise scaling parameters based on Ripley's K-function which enables data clustering at different density scales within the same dataset. Additionally, we provide a method for enhancing the spatial similarity matrix by including a density metric between neighborhoods. We show how our proposed methods for building similarity matrices can improve the results attained by traditional approaches for several well known clustering algorithms on a variety of datasets.

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Cite

Text

Streib and Davis. "Using Ripley's K-Function to Improve Graph-Based Clustering Techniques." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995509

Markdown

[Streib and Davis. "Using Ripley's K-Function to Improve Graph-Based Clustering Techniques." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/streib2011cvpr-using/) doi:10.1109/CVPR.2011.5995509

BibTeX

@inproceedings{streib2011cvpr-using,
  title     = {{Using Ripley's K-Function to Improve Graph-Based Clustering Techniques}},
  author    = {Streib, Kevin and Davis, James W.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {2305-2312},
  doi       = {10.1109/CVPR.2011.5995509},
  url       = {https://mlanthology.org/cvpr/2011/streib2011cvpr-using/}
}