What must an analyst do when using K-means clustering?

When employing K-means clustering, the analyst is required to predetermine the number of clusters in which the data will be divided. This is a fundamental step because K-means is centered around the idea of creating groups, or clusters, based on the input data. By selecting a specific number of clusters, the analyst specifies how many distinct groupings will be formed during the clustering process.

The choice of the number of clusters significantly influences the results of K-means clustering, as it dictates how data points are allocated to different clusters. Selecting too few clusters might result in oversimplified patterns, while too many clusters can lead to overfitting, where random noise is treated as a separate cluster. Thus, accurately determining the appropriate number of clusters is integral to effective clustering and ensures that the resulting groups are meaningful and appropriately characterize the data structure.

The other potential actions related to K-means clustering, such as defining cluster characteristics, preparing data with Z-scores, or running clustering routines on large files, while important, do not constitute the core requirement of K-means. The specification of the number of clusters is what fundamentally guides the clustering results.