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 |
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