From the course: Machine Learning and AI Foundations: Clustering and Association

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Using cluster analysis and decision trees together

Using cluster analysis and decision trees together

From the course: Machine Learning and AI Foundations: Clustering and Association

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Using cluster analysis and decision trees together

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- [Instructor] One of the most common questions I get when folks that I meet learn that cluster analysis is one of my topics of interest is they want to know how to handle all of their categorical variables, and as you've heard me share with you, I usually get concerned that folks are too quick to use their categorical variables in the analysis. K-means does really support it and hierarchical, it's all but impossible. In some newer techniques it's allowed, but the fact that it's allowed, I think, makes people rush into it. So, how should you really do it together? As it turns out, cluster analysis and decision trees are a powerful combination. Here's how it works. You rarely need categories in the cluster analysis itself, so don't lose sleep over the fact that your algorithm of choice or your software tool has a style of cluster analysis that doesn't allow for categorical variables. It's probably fine. What you really want to be doing is focusing on the kinds of variables we've seen…

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