K Means using elbow method

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How to use elbow method to find the best no of clusters to use K Means?
Aug 25 in Data Analytics by praneel
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1 answer to this question.

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Elbow method allows the user to know the best fit number of clusters.

Follow the below steps:

  1. Compute clustering algorithm (e.g., k-means clustering) for different values of k. For instance, by varying k from 1 to 10 clusters.
  2. For each k, calculate the tot.withinss.
  3. Plot the curve of above values against the number of clusters from step 1.
  4. The value at the bend of the plot is considered as the best fit value for no of clusters.
totwss=sapply(1:10, function(k) { kmeans(mtcars$mpg,k)$tot.withinss})
k = data.frame(k = 1:6,totwss = totwss)
ggplot(k,aes(k,totwss))+geom_line()

In this case, you can take 2 or 3 as per your choice.

answered Aug 26 by anonymous
• 32,260 points

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