Accepted Solutions
Hi @pnogau,
Welcome in the Community !
Concerning your questions :
- If you have data with mixed data and modeling type (numerical continuous, ordinal and nominal), then only the Hierarchical Cluster platform will be able to handle such various data type. You can have more info here, Overview of Platforms for Clustering Observations where this table is shown :
You don't need to do the processing of numerical continuous variables beforehand, there are several options to do the pre-processing directly in the platform by specifying data format, type of standardization, and missing data imputation : Launch the Hierarchical Cluster Platform - Not sure to fully understand your second question.
Clustering is used when you don't know beforehand how many "groups"/clusters you have in your data and in which group your observations belong, so it's a unsupervised learning technique. Hierarchical clustering is an interesting technique and platform in JMP, as it enables to perform Two-Way clustering, where your observations are grouped in clusters but also the variables used, to see the similarity and correlations between the variables used. This analysis can be performed in addition of other multivariate platforms like Correlations and Multivariate Techniques or with visualizations done with Graph Builder, to better assess the correlations between your variables.
Also if your binomial variable is some kind of target, you could perform the clustering "blindly" and see how many groups are recommended, and analyze the link between the groups and the binomial variable (which would be a combination of unsupervised learning for clustering, and then supervised learning to analyze the link between clusters and binomial target), or directly specifying that you want 2 clusters in the Hierarchical Clustering platform (which could then be considered as semi-supervised learning, since you already knwo the number of clusters to find and specify it), and see if/how the clustering matches the binomial target variable.
Hope this answer will help you,
Hi @pnogau,
Welcome in the Community !
Concerning your questions :
- If you have data with mixed data and modeling type (numerical continuous, ordinal and nominal), then only the Hierarchical Cluster platform will be able to handle such various data type. You can have more info here, Overview of Platforms for Clustering Observations where this table is shown :
You don't need to do the processing of numerical continuous variables beforehand, there are several options to do the pre-processing directly in the platform by specifying data format, type of standardization, and missing data imputation : Launch the Hierarchical Cluster Platform - Not sure to fully understand your second question.
Clustering is used when you don't know beforehand how many "groups"/clusters you have in your data and in which group your observations belong, so it's a unsupervised learning technique. Hierarchical clustering is an interesting technique and platform in JMP, as it enables to perform Two-Way clustering, where your observations are grouped in clusters but also the variables used, to see the similarity and correlations between the variables used. This analysis can be performed in addition of other multivariate platforms like Correlations and Multivariate Techniques or with visualizations done with Graph Builder, to better assess the correlations between your variables.
Also if your binomial variable is some kind of target, you could perform the clustering "blindly" and see how many groups are recommended, and analyze the link between the groups and the binomial variable (which would be a combination of unsupervised learning for clustering, and then supervised learning to analyze the link between clusters and binomial target), or directly specifying that you want 2 clusters in the Hierarchical Clustering platform (which could then be considered as semi-supervised learning, since you already knwo the number of clusters to find and specify it), and see if/how the clustering matches the binomial target variable.
Hope this answer will help you,
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 ~