The Effective Data Scientist
Cluster Analysis (Episode 22)
Dec 29, 2023
In this podcast, the hosts discuss the basics of cluster analysis and the challenges it presents. They explain the K means clustering approach and the importance of finding the right balance in the number and size of clusters. They explore the process of interpreting clusters and labeling them in medical fields and marketing. The difficulties of applying clustering algorithms to non-continuous variables are discussed, as well as the importance of visualization, standardization, categorical variables, and weighting in understanding clusters.
32:59
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Quick takeaways
- Cluster analysis groups observations based on similarity and helps in assigning meaningful labels based on variables used for clustering.
- Cluster analysis finds applications in medicine and marketing, where it can identify patient subgroups and target specific client preferences.
Deep dives
Understanding Cluster Analysis
Cluster analysis is a technique under unsupervised learning that groups observations based on similarity. It is commonly used for analyzing continuous data and finding patterns within larger sample sizes. Different clustering algorithms, such as K-means clustering, can be used to identify clusters with the desired number of data points. However, finding the right balance of clusters and cluster sizes can be challenging. Interpretation of the clusters is another important aspect, where visualizing the characteristics of each cluster helps in assigning meaningful labels based on variables used for clustering.
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