Hierarchical clustering
Hierarchical clustering is a clustering method that organizes observations into nested groups by successively merging or splitting clusters.
Data Without Labels
Hierarchical clustering is a clustering method that constructs a nested hierarchy of groups by successively merging or splitting clusters.
- How it is used
- Supports customer segmentation by producing nested groups that can be cut and assessed against business needs.
- How deep it goes
- Explains merging, splitting, greedy choices, dendrogram cuts, and a Python workflow; leaves the best cluster count to interpretation.
- Examples
- Uses five numbered observations, three linkage visualizations, and Python plots showing how distance cutoffs change clusters.
- What you can do after
- Apply it via SciPy linkage and fcluster, then inspect dendrograms and plotted groups before selecting a distance cutoff.
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Statistics Slam Dunk
Hierarchical clustering is a clustering method that builds a nested hierarchy of groups based on similarity or distance.
- How it is used
- Segments NBA draft picks to assess whether top-five selections warrant different career expectations from other first-round picks.
- How deep it goes
- Works through variable selection, mean aggregation, Euclidean distances, a distance matrix, complete-linkage hclust(), and dendrogram interpretation.
- Examples
- Uses 2000–2009 NBA first-round pick numbers and mean career win shares, highlighting a two-group split of picks 1–5 and 6–30.
- What you can do after
- Build it from scratch in base R by creating distances, running hclust(), plotting a dendrogram, and inspecting chosen cluster boxes.
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Build a Robo-Advisor with Python (From Scratch)
Hierarchical clustering is a clustering technique that organizes objects into progressively combined or subdivided groups according to a distance measure.
- How it is used
- Organizes correlated assets into an HRP tree before covariance reordering and recursive portfolio-weight allocation.
- How deep it goes
- Derives two distance matrices and implements iterative linkage, while deferring weight calculation to recursive bisection.
- Examples
- Works through eight ETF tickers, a distance matrix, cluster merges, asset ordering, and reordered correlation heatmaps.
- What you can do after
- Build it from scratch by transforming correlations into distances and merging the nearest asset clusters into a link matrix.
Read how Build a Robo-Advisor with Python (From Scratch) explains hierarchical clustering →
Advanced Algorithms and Data Structures
Hierarchical clustering is a clustering technique that forms nested groups by successively merging or dividing clusters.
- How it is used
- Uses unlabeled data to infer related groups and inspect alternative partitions for applications such as customer segmentation.
- How deep it goes
- Explains agglomerative construction, dendrograms, slicing, and parameter effects, but leaves the broader clustering taxonomy beyond the treatment.
- Examples
- Uses two-dimensional point datasets, progressive A–B–C grouping, dendrograms, reachability plots, and customer segmentation.
- What you can do after
- Evaluate or understand nested clusters, choose hierarchy snapshots, and relate implementation needs to disjoint-set data structures.
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Domain-Specific Small Language Models
A clustering approach that builds communities through repeated refinement and aggregation of grouped items.
- How it is used
- Detects communities for GraphRAG indexing and later community summarization.
- How deep it goes
- Explains the move-refine-aggregate loop and modularity gain, but stops short of a full validated implementation.
- Examples
- Uses a combined graph, community summaries, and a custom Python Leiden implementation.
- What you can do after
- Apply it to partition graphs into communities for downstream summarization and query answering.
Read how Domain-Specific Small Language Models explains hierarchical clustering →
Related concepts
- K-means clusteringcontrasts with · K-means clustering partitions data into a specified number of groups by assigning observations to representative centers and iteratively updating those centers to reduce within-group distances.
- OPTICSincludes · OPTICS is a density-based clustering algorithm that orders points and computes reachability distances to reveal cluster structure across varying density thresholds.
- unsupervised learningkind of · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
- clusteringkind of · Clustering is the technique of grouping data items according to similarity or shared characteristics.
- agglomerative clusteringincludes · Agglomerative clustering is a bottom-up hierarchical clustering method that starts with individual items as separate clusters and repeatedly merges clusters according to a linkage criterion.
- dendrogramproduces · A dendrogram is a tree diagram that represents hierarchical relationships among items or nested clusters, including the levels at which they merge or split.
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