Ai Hierarchical Clustering — Free AI & ML Tutorial
Learn Ai Hierarchical Clustering in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Hierarchical Clustering in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Hierarchical Clustering in AI & ML
Hierarchical Clustering builds a tree-like structure (dendrogram) of clusters without needing to specify K upfront. Unlike K-Means, you can cut the dendrogram at any level to get any number of clusters.
Agglomerative (bottom-up): Each point starts as its own cluster. Then repeatedly merge the two closest clusters until one remains. Most common approach.
Divisive (top-down): Start with all points in one cluster, repeatedly split until each point is its own cluster. Less common.
Linkage methods: Single linkage (distance = closest pair between clusters), Complete linkage (distance = farthest pair), Average linkage (distance = average of all pairs). Average and complete linkage generally produce better results.
Ai Hierarchical Clustering — Syntax
# Agglomerative Hierarchical Clustering: # 1. Each point = its own cluster # 2. Find two closest clusters # 3. Merge them # 4. Repeat until 1 cluster (or desired K) # # In sklearn: # from sklearn.cluster import AgglomerativeClustering # model = AgglomerativeClustering(n_clusters=3, linkage='average'
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