Intrinsic Hierarchical Clustering Behavior Recovers Higher Dimensional Shape Information

@article{Ignacio2020IntrinsicHC,
  title={Intrinsic Hierarchical Clustering Behavior Recovers Higher Dimensional Shape Information},
  author={Paul Samuel P. Ignacio},
  journal={2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)},
  year={2020},
  pages={0206-0212}
}
  • Paul Samuel P. Ignacio
  • Published 8 October 2020
  • Computer Science
  • 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)
We show that specific higher dimensional shape information of point cloud data can be recovered by observing lower dimensional hierarchical clustering dynamics. We generate multiple point samples from point clouds and perform hierarchical clustering within each sample to produce dendrograms. From these dendrograms, we take cluster evolution and merging data that capture clustering behavior to construct simplified diagrams that record the lifetime of clusters akin to what zero dimensional… 
3 Citations

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