Which statement about clustering methods is true?

The statement about clustering methods that is true pertains to the relative accuracy of hierarchical clustering compared to K-means clustering for a set number of clusters. Hierarchical clustering constructs a tree of clusters by either a divisive (top-down) or agglomerative (bottom-up) approach, which allows for a flexible exploration of data at various levels of granularity. However, the resulting clusters may not always represent the best separation of data points, especially in datasets with high variability or noise.

K-means clustering, on the other hand, is an optimization-based method where the number of clusters must be predefined. It attempts to minimize the variance within each cluster, often resulting in more distinct and tight clusters when the assumptions of K-means are met (such as spherical cluster shapes and equal cluster sizes). Thus, depending on the characteristics of the dataset and the specific clustering goals, K-means can indeed perform better and yield more accurate results than hierarchical clustering for the same number of clusters.

In summary, the assertion that hierarchical clustering can be less accurate than K-means for a given number of clusters accurately reflects the situational performance of these methods, dependent on the specific data characteristics and validation measures employed.