Back to GalleryHierarchical Clustering
Hierarchical Clustering builds a tree of clusters (dendrogram) in a bottom-up (agglomerative) manner. It starts by treating each data point as an individual cluster and iteratively merges the closest pair of clusters.
Key Parameters & Visual Influence:
• Linkage Method: Defines how the distance between two clusters is measured. 'Ward' minimizes the variance of merged clusters. 'Single' uses the minimum distance between points (creating long, chaining clusters). 'Complete' uses maximum distance, producing compact, spherical clusters.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label