distlim_kmeans_clustering: Categorize multi dimension data based on the relative...
In hmito/hmRLib: Helper library for numerical analysis with R
distlim_kmeans_clustering R Documentation
Categorize multi dimension data based on the relative distance vs cluster size using kmeans function
Description
Based on the kmeans clustering algorithm, find optimal cluster num by keeping multiple cluster are not overlapped.
Usage
distlim_kmeans_clustering(data, rate = 2, iter = 10, kmax = 10)
Arguments
data
Matrix data for clustering. Row is element and Col is axis.
rate
Threshold rate of clustersize / distance
iter
Number of iteration for kmeans.
kmax
Max cluster number.
Value
List with size: number of cluster and group: #cluster of each element.
hmito/hmRLib documentation built on March 13, 2024, 9:41 p.m.
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.
| distlim_kmeans_clustering | R Documentation |
Categorize multi dimension data based on the relative distance vs cluster size using kmeans function
Description
Based on the kmeans clustering algorithm, find optimal cluster num by keeping multiple cluster are not overlapped.
Usage
distlim_kmeans_clustering(data, rate = 2, iter = 10, kmax = 10)
Arguments
data |
Matrix data for clustering. Row is element and Col is axis. |
rate |
Threshold rate of clustersize / distance |
iter |
Number of iteration for kmeans. |
kmax |
Max cluster number. |
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.