Hierarchical clustering: merge nearest, read the dendrogram

1. 1Apply hierarchical clustering to a dataset using a library like Scikit-learn
2. 2Visualize the resulting dendrogram to understand data relationships
3. 3Use the dendrogram to determine the optimal number of clusters
4. 4Compare the results of hierarchical clustering with K-Means for different cluster counts
5. 5Configure the clustering algorithm to use different distance metrics or linkage methods

Data scientists and analysts can benefit from hierarchical clustering to identify patterns in their data without predefining cluster counts, while data engineers can use this technique to inform their data pipeline designs

💡 Hierarchical clustering allows you to visualize data relationships and determine the optimal number of clusters without predefining it

📊 Ditch the guesswork of K-Means cluster count with hierarchical clustering! 📈