Which of the following statements about K-means clustering is true?
Correct Answer: K-means can be sensitive to the initial placement of centroids.
- Step 1: Understand what K-means clustering is. It is a method used to group data points into clusters based on their features.
- Step 2: Know that K-means starts by choosing a certain number of points (called centroids) to represent the center of each cluster.
- Step 3: Realize that the initial placement of these centroids can vary each time you run the K-means algorithm.
- Step 4: Understand that if the centroids are placed differently at the start, the final clusters formed can also be different.
- Step 5: Conclude that this sensitivity to initial placement means that K-means can produce different results on different runs.
No concepts available.
