Which of the following is NOT a characteristic of K-means clustering?

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Question: Which of the following is NOT a characteristic of K-means clustering?

Options:

Correct Answer: It can handle non-spherical clusters well

Solution:

K-means clustering struggles with non-spherical clusters, as it assumes clusters are spherical and equally sized.

Which of the following is NOT a characteristic of K-means clustering?

Which of the following is NOT a characteristic of K-means clustering?

Step 1: Understand what K-means clustering is. It is a method used to group data points into clusters based on their similarities.
Step 2: Know that K-means clustering assumes that the clusters are spherical in shape. This means it expects the clusters to look like balls.
Step 3: Realize that K-means also assumes that all clusters are of similar size. This means it thinks each group of data points has about the same number of points.
Step 4: Identify that if the actual data has clusters that are not spherical or are of different sizes, K-means will not perform well.
Step 5: Conclude that the characteristic of struggling with non-spherical clusters is NOT a feature of K-means clustering, but rather a limitation.

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