In clustering, inertia is also called the measure that sums squared distances from points to their centroid. Which label correctly names this metric?

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Multiple Choice

In clustering, inertia is also called the measure that sums squared distances from points to their centroid. Which label correctly names this metric?

In clustering, inertia refers to the sum of squared distances from each point to its cluster’s centroid, which measures how compact each cluster is. This metric is called Within-Cluster Sum of Squares (WCSS); it captures the total within-cluster dispersion and is the quantity most commonly used as inertia in algorithms like k-means. By contrast, Between-Cluster Sum of Squares (BCSS) measures how far cluster centroids are from the overall mean, and Total Variance equals WCSS plus BCSS. The Average Distance to Centroid is not the standard inertia since inertia involves a sum of squared distances, not an average.

In clustering, inertia refers to the sum of squared distances from each point to its cluster’s centroid, which measures how compact each cluster is. This metric is called Within-Cluster Sum of Squares (WCSS); it captures the total within-cluster dispersion and is the quantity most commonly used as inertia in algorithms like k-means. By contrast, Between-Cluster Sum of Squares (BCSS) measures how far cluster centroids are from the overall mean, and Total Variance equals WCSS plus BCSS. The Average Distance to Centroid is not the standard inertia since inertia involves a sum of squared distances, not an average.

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