When evaluating large data sets, it is customary to cluster large data sets using the k-means to reduce the computation of measures during each iteration compared to hierarchical clustering methods.

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K-means clustering is generally preferred for large datasets because it is computationally more efficient than hierarchical clustering methods, especially in terms of reducing the computation required during each iteration. Hierarchical methods, which often require computing and updating distance matrices, can be computationally intensive and less scalable for large datasets.

When Evaluating Large Data Sets, It Is Customary to Cluster