Principal components are usually easier to interpret than the cluster components.

Question

Quizplus · Accepted Answer

Principal components are linear combinations of the original variables created in principal component analysis (PCA) to capture maximum variance, and they often do not correspond to intuitive or easily interpretable dimensions. In contrast, cluster components (or centroids in clustering analysis) represent the center of a group of similar data points, which can sometimes be more directly interpreted in terms of the original features.

Principal Components Are Usually Easier to Interpret Than the Cluster