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:
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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.
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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.
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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.
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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.
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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.
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