distlim_hclust_clustering: Categorize multi dimension data based on the relative...
In hmito/hmRLib: Helper library for numerical analysis with R
distlim_hclust_clustering R Documentation
Categorize multi dimension data based on the relative distance vs cluster size using hclust function
Description
Based on the h-clustering algorithm, find optimal cluster num by keeping multiple cluster are not overlapped.
Usage
distlim_hclust_clustering(data, thr = 5, kmax = 10, method = "ward.D2")
Arguments
data
Matrix data for clustering. Row is element and Col is axis.
thr
Threshold value of the relative distance/size of clusters.
kmax
Max cluster number.
method
Clustering method in hclust.
Value
List with size: number of cluster and group: #cluster of each element.
hmito/hmRLib documentation built on Sept. 8, 2024, 4:49 p.m.
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| distlim_hclust_clustering | R Documentation |
Categorize multi dimension data based on the relative distance vs cluster size using hclust function
Description
Based on the h-clustering algorithm, find optimal cluster num by keeping multiple cluster are not overlapped.
Usage
distlim_hclust_clustering(data, thr = 5, kmax = 10, method = "ward.D2")
Arguments
data |
Matrix data for clustering. Row is element and Col is axis. |
thr |
Threshold value of the relative distance/size of clusters. |
kmax |
Max cluster number. |
method |
Clustering method in hclust. |
Value
List with size: number of cluster and group: #cluster of each element.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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