ML Anthology

Fuzzy Algorithms to Find Linear and Planar Clusters and Their Applications

Raghu Krishnapuram, Chih-Pin Freg
CVPR 1991 pp. 426-431
doi:10.1109/CVPR.1991.139728 /cvpr/1991/krishnapuram1991cvpr-fuzzy/

Abstract

A fuzzy adaptive distance dynamic clusters (FADDC) algorithm, which is specially designed to search for clusters that lie in subspaces (such as lines, and (hyper)planes) is presented. One major drawback of all clustering algorithms is that the number of clusters has to be known a priori. A novel compatible cluster merging (CCM) technique, which finds the optimum number of clusters in an efficient way, is proposed. Such subspace clustering techniques may be used for character recognition and to obtain straight-line descriptions of an edge image. They may also be used to obtain planar approximations of 3-D (range) data. The effectiveness of the proposed algorithms in several such situations is demonstrated with real data.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Krishnapuram and Freg. "Fuzzy Algorithms to Find Linear and Planar Clusters and Their Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1991. doi:10.1109/CVPR.1991.139728

Markdown

[Krishnapuram and Freg. "Fuzzy Algorithms to Find Linear and Planar Clusters and Their Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1991.](https://mlanthology.org/cvpr/1991/krishnapuram1991cvpr-fuzzy/) doi:10.1109/CVPR.1991.139728

BibTeX

@inproceedings{krishnapuram1991cvpr-fuzzy,
  title     = {{Fuzzy Algorithms to Find Linear and Planar Clusters and Their Applications}},
  author    = {Krishnapuram, Raghu and Freg, Chih-Pin},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1991},
  pages     = {426-431},
  doi       = {10.1109/CVPR.1991.139728},
  url       = {https://mlanthology.org/cvpr/1991/krishnapuram1991cvpr-fuzzy/}
}