Elbow Method in R: Find Optimal Clusters for K-Means
Key Points K-means clustering is an unsupervised machine learning algorithm that
partitions a dataset into k clusters based on the similarity of the data points.
The elbow method is a technique that helps you find the optimal value of k
for k-means clustering.
The elbow method involves running k-means clustering on a range of k values
and calculating a cluster quality measure for each value.
The cluster quality measure is usually the within-cluster sum of squares
(WCSS), the sum of squared distances between each data point and its cluster
centroid.
The elbow method plots the cluster quality measure against k and looks for
an "elbow" in the curve.
The elbow point is where the cluster quality measure stops decreasing
rapidly as you increase k.
The value of k at the elbow point is the optimal number of clusters for the
dataset.
The E lbow method could be better, as sometimes there may not be a clear
elbow point or more than one elbow point in the curve. In such cases, you may ne…
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