How to Find Optimal Clusters in R? | R-bloggers

How to Find Optimal Clusters in R?
Posted on September 10, 2022 by Jim in R bloggers | 0 Comments
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How to Find Optimal Clusters in R, K-means clustering is one of the most widely used clustering techniques in machine learning.
With the K-means clustering technique, each observation in a dataset is assigned to one of K clusters.
The ultimate goal is to have K clusters in which the observations are relatively similar to one another within each cluster and considerably dissimilar from one another within different clusters.
The first stage in k-means clustering is to decide on a value for K or the number of clusters we want to group the observations into.
The elbow method is one of the most popular approaches to choosing a value for K.
It entails plotting the total inside the sum of squares on the y-axis and the number of clusters on the x-axis to locate the plot’s “elbow” or bend.
The best number of clusters to utilize in the k-means clustering algorithm is indicated by the location on the x-axis where the “elbow” occurs.
The elbow method in R is demonstrated in the example that follows.
How to Find Optimal Clusters in R
We’ll use the USArrests dataset from R for this example, which includes the proportion of the population residing in urban areas in each state, or UrbanPop, as well as the number of murder, assault, and rape arrests made per 100,000 citizens in each state of the United States in 1973.
The dataset may be loaded using the code below, which also demonstrates how to delete rows with blank values and scale each variable in the dataset to have a mean and standard deviation of 0 and 1, respectively.
Now let’s load the data
df