The optimal number of clusters (clustering) depends on the data and the desired level of granularity. Here are some common approaches:
- Elbow Method: WCSS and elbow method: Plot the within-cluster sum of squares (WCSS) as a function of the number of clusters. The optimal number of clusters is often the point where the WCSS starts to decrease slowly.
- Silhouette Analysis: Calculate the silhouette coefficient for each data point, which measures how similar a data point is to its own cluster compared to other clusters. The optimal number of clusters 1 is often the one that maximizes the average silhouette coefficient.T