What is the purpose of the elbow method in clustering?

What is the purpose of the elbow method in clustering?

Step 1: Understand that clustering is a way to group similar data points together.
Step 2: Know that we need to decide how many groups (clusters) to create from the data.
Step 3: The elbow method is a technique used to find the best number of clusters.
Step 4: To use the elbow method, we first create clusters for different numbers of groups (like 1, 2, 3, etc.).
Step 5: For each number of clusters, we calculate how well the data fits into those clusters, which is called explained variance.
Step 6: We then plot the explained variance on a graph with the number of clusters on the x-axis and explained variance on the y-axis.
Step 7: Look for a point on the graph where the explained variance starts to level off, forming an 'elbow' shape.
Step 8: The number of clusters at this elbow point is considered the optimal number of clusters.

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