As in previous blogs, here I’m interested in sharing about the hierarchical clustering machine learning method.
Hierarchical clustering is also a powerful machine learning algorithm for data analysis that allows us to explore the relationships between different variables in a dataset. By creating a dendrogram, we can see how different states are related to each other based on a variety of factors, such as race, gender, or age.
One of the key benefits of hierarchical clustering is its ability to handle both numerical and categorical data. This means that we can include a wide range of variables in our analysis. By looking at the relationships between such variables, we can gain a deeper understanding of the complex factors that shape our dataset model.
Another advantage of hierarchical clustering is its flexibility. We can adjust the parameters of the analysis to focus on specific aspects of the data. This allows us to tailor our analysis to the specific questions we want to answer, and to uncover insights that might be missed by other methods.