find_best_k_for_kmeans: Find the optimal number of clusters for k-means
In MapperAlgo: Topological Data Analysis: Mapper Algorithm
find_best_k_for_kmeans R Documentation
Find the optimal number of clusters for k-means
Description
This function calculates the total within-cluster sum of squares (WSS) for a range
of cluster numbers and identifies the best number of clusters (k) based on the
elbow method.
Usage
find_best_k_for_kmeans(dist_object, max_clusters = 10)
Arguments
dist_object
A distance matrix or data frame containing the data to be clustered.
max_clusters
The maximum number of clusters to test for k-means. Default is 10.
Value
The optimal number of clusters (k) based on the elbow method.
MapperAlgo documentation built on June 21, 2025, 9:08 a.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.
| find_best_k_for_kmeans | R Documentation |
Find the optimal number of clusters for k-means
Description
This function calculates the total within-cluster sum of squares (WSS) for a range of cluster numbers and identifies the best number of clusters (k) based on the elbow method.
Usage
find_best_k_for_kmeans(dist_object, max_clusters = 10)
Arguments
dist_object |
A distance matrix or data frame containing the data to be clustered. |
max_clusters |
The maximum number of clusters to test for k-means. Default is 10. |
Value
The optimal number of clusters (k) based on the elbow method.
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.