Machine LearningLesson 83Free preview
Hierarchical Clustering
This lesson is part of Python · All levels
Summary
Hierarchical clustering groups data in stages so that the result is a cluster tree. The agglomerative approach works from below: each point starts as its own cluster and then the two closest clusters are combined repeatedly. The results were divided by a dendrogram showing the degree of integration of each cluster. Distance measurement methods such as ward, single, and complete determine how to select pairs of clusters that are combined.
Key points
- Hierarchical clustering forms a tree cluster
- The agglomerative approach combines clusters from the bottom up
- Dendrograms visualize the hierarchy of cluster mergers
- The ward method combines clusters that raise the smallest variation
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