Question

Which of the following are false about K-Means Algorithm? Please choose all those apply.

Answer 1

To determine which statements about the K-Means algorithm are false, it would be helpful to know the specific statements you are considering. However, I can provide you with some common misconceptions and inaccuracies about K-Means that may help identify false statements:

1. **K-Means does not require prior knowledge of the number of clusters**: This statement is false. K-Means requires the user to specify the number of clusters (K) in advance.

2. **K-Means can handle clusters of arbitrary shape**: This statement is false. K-Means performs best on spherical clusters and is not effective for clusters with non-convex shapes.

3. **K-Means can be used with categorical data**: This statement is false. K-Means is designed for continuous numerical data, as it relies on calculating distances (typically Euclidean), which is not suitable for categorical variables.

4. **K-Means guarantees finding the global optimum**: This statement is false. K-Means can converge to a local minimum, and the results can depend on the initial choice of centroids.

5. **K-Means is insensitive to outliers**: This statement is false. K-Means is sensitive to outliers because they can disproportionately affect the position of the cluster centroids.

If you have specific statements you'd like evaluated, please provide them, and I can help identify which are false.