find_best_k_for_kmeans

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.

The Mapper algorithm from Topological Data Analysis, the steps are as follows 1. Define a filter (lens) function on the data. 2. Perform clustering within each level set. 3. Generate a complex from the clustering results.

ChiChien Wang

MapperAlgo

Topological Data Analysis: Mapper Algorithm

Paul Pearson

Daniel Muellner

Gurjeet Singh

find_best_k_for_kmeans function

<dl><dt>dist_object</dt>
<dd>A distance matrix or data frame containing the data to be clustered.</dd>
<dt>max_clusters</dt>
<dd>The maximum number of clusters to test for k-means. Default is 10.</dd></dl>

Arguments

Find the optimal number of clusters for k-means — find_best_k_for_kmeans

<dl>

<dt>dist_object</dt>
<dd>A distance matrix or data frame containing the data to be clustered.</dd>


<dt>max_clusters</dt>
<dd>The maximum number of clusters to test for k-means. Default is 10.</dd>

</dl>

find_best_k_for_kmeans: Find the optimal number of clusters for k-means

Description

Usage

Value

Arguments