HCV: Hierarchical Clustering from Vertex-Links

Hierarchical clustering for spatial data, which requires clustering results not only homogeneous in non-geographical features among samples but also geographically close to each other within a cluster. It modified typically used hierarchical agglomerative clustering algorithms for introducing the spatial homogeneity, by considering geographical locations as vertices and converting spatial adjacency into whether a shared edge exists between a pair of vertices (Tzeng & Hsu, 2022) <arXiv:2201.08302>. The constraints of the vertex links automatically enforce the spatial contiguity property at each step of iterations. In addition, methods to find an appropriate number of clusters and to report cluster members are also provided.

Version: 1.2.0
Depends: R (≥ 4.0.0)
Imports: BLSM (≥ 0.1.0), cluster, geometry (≥ 0.4.5), graphics, grDevices, M3C (≥ 1.12.0), MASS, Matrix, rgeos (≥ 0.5.1), sp (≥ 1.4.2)
Suggests: alphahull, knitr, fields (≥ 11.4)
Published: 2022-02-22
Author: ShengLi Tzeng [cre, aut], Hao-Yun Hsu [aut]
Maintainer: ShengLi Tzeng <slt.cmu at gmail.com>
License: LGPL-3
NeedsCompilation: no
CRAN checks: HCV results


Reference manual: HCV.pdf


Package source: HCV_1.2.0.tar.gz
Windows binaries: r-devel: HCV_1.2.0.zip, r-release: HCV_1.2.0.zip, r-oldrel: HCV_1.2.0.zip
macOS binaries: r-release (arm64): HCV_1.2.0.tgz, r-oldrel (arm64): HCV_1.2.0.tgz, r-release (x86_64): HCV_1.2.0.tgz, r-oldrel (x86_64): HCV_1.2.0.tgz
Old sources: HCV archive


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