Dendrogram
A dendrogram is a tree diagram that represents hierarchical relationships among items or nested clusters, including the levels at which they merge or split.
Data Without Labels
A dendrogram is a tree diagram that represents the nested grouping and merging of observations in hierarchical clustering.
- How it is used
- Guides hierarchical-clustering users from nested merges to a defensible number of final clusters.
- How deep it goes
- Defines branches, leaves, and merge heights, then gives a cutting procedure; it stops short of automatic optimum selection.
- Examples
- Uses candidate cuts, merge-height comparisons, Python linkage workflows, truncated plots, and scatterplots of resulting clusters.
- What you can do after
- Apply it via SciPy and matplotlib to inspect hierarchical merges, select a distance threshold, and visualize resulting clusters.
Statistics Slam Dunk
A dendrogram is a branching diagram that represents hierarchical relationships or nested cluster structure.
- How it is used
- Uses dendrograms to segment observations and discover structure when no response variable is being predicted.
- How deep it goes
- Explains the merge process, Euclidean distances, complete linkage, plotting, and cluster cuts without deriving clustering internals.
- Examples
- Uses NBA first-round pick numbers, average career win shares, a distance matrix, and a two-cluster dendrogram.
- What you can do after
- Build it from scratch in R by preparing variables, calculating distances, fitting hierarchical clustering, plotting the tree, and selecting clusters.
Advanced Algorithms and Data Structures
A dendrogram is a tree diagram that represents the hierarchical arrangement of clusters.
- How it is used
- Supports exploring hierarchical clusterings and selecting flat partitions at different levels.
- How deep it goes
- Explains tree structure, cluster merges, threshold selection, and branch-specific thresholds, without detailing construction internals.
- Examples
- Uses an OPTICS result with ϵMAX=2.0, minPoints=3, clusters C1–C7, and super-clusters CA–CF.
- What you can do after
- Evaluate hierarchical cluster structure and select flat clusterings by inspecting sections or applying thresholds.
Read how Advanced Algorithms and Data Structures explains dendrogram →
Related concepts
- hierarchical clusteringproduced by · Hierarchical clustering is a clustering method that organizes observations into nested groups by successively merging or splitting clusters.
- Matplotlibused by · Matplotlib is an open-source Python library for creating static, animated, and interactive data visualizations.
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