What type of data is K-means clustering best suited for?

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Q: What type of data is K-means clustering best suited for?

Solution: K-means clustering is best suited for numerical data, as it relies on calculating distances between data points.

Steps: 5

Step 1: Understand what K-means clustering is. It is a method used to group similar data points together.
Step 2: Know that K-means clustering works by finding the center of groups (clusters) of data points.
Step 3: Realize that to find these centers, K-means needs to calculate distances between data points.
Step 4: Recognize that distances can be easily calculated for numerical data (like height, weight, or temperature).
Step 5: Conclude that K-means clustering is best suited for numerical data because it relies on these distance calculations.