title: How to explain a higher percentage of point variability using kmeans clustering?
body: <p>I'm doing some kmeans clustering:</p>
  
  <p><img src="https://i.stack.imgur.com/ntVF4.png" alt="enter image description here"></p>
  
  <p><strong>Regardless of how many clusters I choose to use, the percentage of point variability does not change:</strong></p>
  
  <p><img src="https://i.stack.imgur.com/5woxO.png" alt="enter image description here"></p>
  
  <p>Here's how I am plotting my data:</p>
  
  <pre><code># Prepare Data
  mydata &lt;- read.csv("~/student-mat.csv", sep=";")
  
  # Let's only grab the numeric columns
  mydata &lt;- mydata[,c("age","Medu","Fedu","traveltime","studytime","failures","fam
  
  mydata &lt;- na.omit(mydata) # listwise deletion of missing
  mydata &lt;- scale(mydata) # standardize variables ibrary(ggplot2)
  
  # K-Means Clustering with 5 clusters
  fit &lt;- kmeans(mydata, 5) #to change number of clusters, I change the "5"
  
  # Cluster Plot against 1st 2 principal components
  
  # vary parameters for most readable graph
  library(cluster)
  clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
     labels=0, lines=0)
  </code></pre>
  
  <p><strong>How do we affect the percentage of point variability?</strong></p>