How can we determine the optimal number of clusters in a clustering algorithm?
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Another approach is the silhouette method, which calculates the average distance between a data point and all other points in its own cluster compared to the average distance to points in the nearest neighboring cluster. The number of clusters that maximizes the average silhouette score is considered optimal.
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One commonly used method is the elbow method, where you plot the within-cluster sum of squares (WCSS) against the number of clusters. The elbow point on the graph corresponds to the optimal number of clusters, where increasing the number of clusters does not significantly decrease the WCSS anymore.
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