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Methods of Hierarchical Clustering
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We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.
Forward citations
Cited by 2 Pith papers
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Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management
FTBUILDER automatically constructs hierarchical feature trees for software artifact libraries using embeddings, clustering, and LLM summarization.
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ARIA: Training Language Agents with Intention-Driven Reward Aggregation
Clustering language-agent actions into shared intentions and averaging their rewards reduces reward variance and improves policy performance in open-ended dialogue tasks.
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