K-means clustering aims to minimize which specific metric, also known as the within-cluster sum-of-squares (WCSS)?
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Inertia
Inertia measures how internally coherent clusters are. While K-means is guaranteed to converge to a local minimum of inertia, it is not guaranteed to find the global optimum, which is why multiple initializations are often used.
Also asked as:
- In K-means clustering, what term is used for the within-cluster sum-of-squares (WCSS) metric that the algorithm seeks to minimize?