In a case study using K-Means clustering, what is a common method to determine t

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Question: In a case study using K-Means clustering, what is a common method to determine the optimal number of clusters?

Options:

Correct Answer: Elbow method

Solution:

The Elbow method helps identify the optimal number of clusters by plotting the explained variance against the number of clusters.

In a case study using K-Means clustering, what is a common method to determine the optimal number of clusters?

In a case study using K-Means clustering, what is a common method to determine the optimal number of clusters?

Step 1: Choose a range of cluster numbers to test, for example, from 1 to 10.
Step 2: For each number of clusters, run the K-Means algorithm to group the data.
Step 3: Calculate the explained variance (or inertia) for each clustering result. This shows how well the data is grouped.
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 a point on the plot where the explained variance starts to level off. This point is called the 'elbow'.
Step 6: The number of clusters at the elbow point is considered the optimal number of clusters.

K-Means Clustering – A method of partitioning data into distinct groups based on feature similarity.
Elbow Method – A technique used to determine the optimal number of clusters by analyzing the variance explained as a function of the number of clusters.