问题如下：
Which of the following machine learning techniques is most appropriate for executing Step 2:
选项：
A.K-Means Clustering
Principal Components Analysis (PCA)
Classification and Regression Trees (CART)
解释：
A is correct. K-Means clustering is an unsupervised machine learning algorithm which repeatedly partitions observations into a fixed number, k, of nonoverlapping clusters (i.e., groups).
B is incorrect. Principal Components Analysis is a long-established statistical method for dimension reduction, not clustering. PCA aims to summarize or reduce highly correlated features of data into a few main, uncorrelated composite variables.
C is incorrect. CART is a supervised machine learning technique that is most commonly applied to binary classification or regression.
Step 2 的文字是否可以解读一下？从哪些描述可以得出是unsupervised learning? 我能读出是分类，但是无法判别是supervised or unsupervised learning.
