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The Elbow Method is a popular technique used to determine the optimal number of clusters in K-means clustering. It involves analyzing the variance within clusters for different values of K and selecting the point where the decrease in variance begins to level off. This helps in choosing a K that balances simplicity and accuracy.
Understanding the Elbow Method
The method plots the sum of squared distances (inertia) between data points and their respective cluster centers for various values of K. As K increases, the inertia decreases. The goal is to find the point where the rate of decrease sharply changes, forming an “elbow” in the plot.
Steps to Calculate the Optimal K
- Run K-means clustering for a range of K values (e.g., 1 to 10).
- Calculate the inertia for each K.
- Plot the inertia against K.
- Identify the point where the decrease in inertia slows down significantly.
- Select that K as the optimal number of clusters.
Interpreting the Results
The “elbow” point on the plot indicates the optimal K. If the plot does not show a clear elbow, consider other methods or domain knowledge to select the best number of clusters. The goal is to choose a K that minimizes within-cluster variance without overfitting.