Pith. sign in

REVIEW 3 cited by

Methods of Hierarchical Clustering

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1105.0121 v1 pith:2DNTCPBR submitted 2011-04-30 cs.IR cs.CVmath.STstat.MLstat.TH

classification cs.IRcs.CVmath.STstat.MLstat.TH
keywords hierarchicalclusteringalgorithmefficientgrid-basedagglomerativealgorithmsapproaches
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Security and Privacy Taxonomy Generation from Mobile App Reviews

    cs.CL 2026-08 conditional novelty 6.0 of 10

    TaxoScale scales taxonomy construction to 601,257 privacy/security app-review pseudo-labels, reporting higher path, level, coverage, and novelty scores than four baselines evaluated on a 100K subset.

  2. Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management

    cs.SE 2025-06 conditional novelty 6.0 of 10

    FTBUILDER automatically constructs hierarchical feature trees for software artifact libraries using embeddings, clustering, and LLM summarization.

  3. ARIA: Training Language Agents with Intention-Driven Reward Aggregation

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Clustering language-agent actions into shared intentions and averaging their rewards reduces reward variance and improves policy performance in open-ended dialogue tasks.

Pith tools