What is a common initialization method for K-means clustering?

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Question: What is a common initialization method for K-means clustering?

Options:

Correct Answer: Randomly selecting data points as initial centroids

Solution:

A common initialization method for K-means is to randomly select data points from the dataset to serve as the initial centroids.

What is a common initialization method for K-means clustering?

What is a common initialization method for K-means clustering?

Step 1: Understand that K-means clustering is a method used to group data points into clusters.
Step 2: Know that each cluster has a center point called a centroid.
Step 3: To start the K-means algorithm, we need to choose initial positions for these centroids.
Step 4: A common way to choose these initial centroids is to randomly select a few data points from the dataset.
Step 5: These randomly selected data points will be the starting points for the centroids in the K-means algorithm.

K-means Clustering Initialization – K-means clustering often starts by selecting initial centroids, which can significantly affect the algorithm's convergence and final results.