问题如下：
Paul suggests the following step which would be repeated every quarter.
Step 2 We utilize ML techniques to divide our investable universe of about 10,000 stocks into 20 different groups, based on a wide variety of the most relevant financial and non-financial characteristics. The idea is to prevent unintended portfolio concentration by selecting stocks from each of these distinct groups.
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.
老师您好，supervised ML中的classification和unsupervised ML中的clustering总感觉分不太清，怎样判断到底是不是对数据贴了标签呢？
