What is a common method to determine the optimal number of clusters in K-means?

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Question: What is a common method to determine the optimal number of clusters in K-means?

Options:

Correct Answer: Elbow method

Solution:

The Elbow method is commonly used to determine the optimal number of clusters by plotting the explained variance against the number of clusters.

What is a common method to determine the optimal number of clusters in K-means?

What is a common method to determine the optimal number of clusters in K-means?

Step 1: Choose a range of numbers for clusters (like 1 to 10).
Step 2: For each number of clusters, run the K-means algorithm.
Step 3: Calculate the explained variance (how well the clusters fit the data) for each number of clusters.
Step 4: Create a plot with the number of clusters on the x-axis and the explained variance on the y-axis.
Step 5: Look for the 'elbow' point in the plot where the explained variance starts to level off.
Step 6: The number of clusters at the elbow point is considered the optimal number.

K-means Clustering – A method used in unsupervised machine learning to partition data into K distinct clusters based on feature similarity.
Elbow Method – A technique for determining the optimal number of clusters by plotting the explained variance and identifying the point where the rate of increase sharply decreases.