Hi. @Victor_G
Thank you very much for your assistance and patient response to my question on the forum. Your answers and suggestions have provided me with valuable insights, and I believe that the issue I was facing has been tentatively resolved.
Question 1: Since my data contains mixed types of variables, it is indeed necessary to perform hierarchical clustering. Furthermore, following your advice, I carefully reread "Launch the Hierarchical Cluster Platform" and found the solution: To address the issue of different measurement scales for continuous and ordinal columns, it seems I should standardize the continuous and discrete variables first, and then select "Unstandardized" under "Standardize By."Standardize By
Question 2: Your understanding of my doubts was very accurate, and your response has given me important inspiration. In fact, I aim to use cluster analysis to discover different clusters within a vast dataset (individuals) and to conduct visual analysis to explore the potential relationships between more than twenty variables, which is an unsupervised machine learning task. However, to obtain more ideal clustering results, it seems that choosing a certain binomial variable under "By" yields very satisfactory clustering outcomes. I am pondering whether this has now become semi-supervised or supervised learning.By
Actually, I was fortunate enough to get in touch with an engineer responsible for JMP's university business in China, and I am planning to further verify my conjecture with the engineer. If you are interested, I will share the answers I receive with you.
I was very excited to receive your reply! Wishing you a happy life and smooth work ~