{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:4I2UN5ZRL3SZOZPZD6J6CJEMUB","short_pith_number":"pith:4I2UN5ZR","schema_version":"1.0","canonical_sha256":"e23546f7315ee59765f91f93e1248ca071661366c72f0f824f4295294b545556","source":{"kind":"arxiv","id":"1109.2378","version":1},"attestation_state":"computed","paper":{"title":"Modern hierarchical, agglomerative clustering algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"stat.ML","authors_text":"Daniel M\\\"ullner","submitted_at":"2011-09-12T05:49:11Z","abstract_excerpt":"This paper presents algorithms for hierarchical, agglomerative clustering which perform most efficiently in the general-purpose setup that is given in modern standard software. Requirements are: (1) the input data is given by pairwise dissimilarities between data points, but extensions to vector data are also discussed (2) the output is a \"stepwise dendrogram\", a data structure which is shared by all implementations in current standard software. We present algorithms (old and new) which perform clustering in this setting efficiently, both in an asymptotic worst-case analysis and from a practic"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1109.2378","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-09-12T05:49:11Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"85c060be32bc1a426f52431916f1b54a479ba0bb1937f5654c9f3b4ffcb90f20","abstract_canon_sha256":"fbdbc5185596e25281a7dbffd5f82b60d1d81710e15995346679cead7c9cec79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:13:37.820325Z","signature_b64":"PVWSiKpZ6pYjEGLTCGQrimvBlfal78VemkTYeHOTf6geNapFnlnP86X7ngf+QzWSU8jJhiJ0IjY6XzxGSgOUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e23546f7315ee59765f91f93e1248ca071661366c72f0f824f4295294b545556","last_reissued_at":"2026-05-18T04:13:37.819571Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:13:37.819571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modern hierarchical, agglomerative clustering algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"stat.ML","authors_text":"Daniel M\\\"ullner","submitted_at":"2011-09-12T05:49:11Z","abstract_excerpt":"This paper presents algorithms for hierarchical, agglomerative clustering which perform most efficiently in the general-purpose setup that is given in modern standard software. Requirements are: (1) the input data is given by pairwise dissimilarities between data points, but extensions to vector data are also discussed (2) the output is a \"stepwise dendrogram\", a data structure which is shared by all implementations in current standard software. We present algorithms (old and new) which perform clustering in this setting efficiently, both in an asymptotic worst-case analysis and from a practic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1109.2378","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1109.2378","created_at":"2026-05-18T04:13:37.819696+00:00"},{"alias_kind":"arxiv_version","alias_value":"1109.2378v1","created_at":"2026-05-18T04:13:37.819696+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1109.2378","created_at":"2026-05-18T04:13:37.819696+00:00"},{"alias_kind":"pith_short_12","alias_value":"4I2UN5ZRL3SZ","created_at":"2026-05-18T12:26:20.644004+00:00"},{"alias_kind":"pith_short_16","alias_value":"4I2UN5ZRL3SZOZPZ","created_at":"2026-05-18T12:26:20.644004+00:00"},{"alias_kind":"pith_short_8","alias_value":"4I2UN5ZR","created_at":"2026-05-18T12:26:20.644004+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":7,"sample":[{"citing_arxiv_id":"2605.23696","citing_title":"Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2605.23655","citing_title":"CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2310.07379","citing_title":"Causal Unsupervised Semantic Segmentation","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2602.06470","citing_title":"Improve Large Language Model Systems with User Logs","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"2605.20962","citing_title":"No-regret optimization of time-varying bilevel problems","ref_index":73,"is_internal_anchor":true},{"citing_arxiv_id":"2510.14063","citing_title":"Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming","ref_index":59,"is_internal_anchor":true},{"citing_arxiv_id":"2602.08280","citing_title":"ClusterChirp: Scalable Interactive Exploration of Omics Data with Natural Language-Guided Analysis","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2604.09812","citing_title":"Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08070","citing_title":"VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selection","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19177","citing_title":"Multiscale Cochran-Mantel-Haenszel Scanning for Conditional Dependency","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21649","citing_title":"GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion","ref_index":145,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB","json":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB.json","graph_json":"https://pith.science/api/pith-number/4I2UN5ZRL3SZOZPZD6J6CJEMUB/graph.json","events_json":"https://pith.science/api/pith-number/4I2UN5ZRL3SZOZPZD6J6CJEMUB/events.json","paper":"https://pith.science/paper/4I2UN5ZR"},"agent_actions":{"view_html":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB","download_json":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB.json","view_paper":"https://pith.science/paper/4I2UN5ZR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1109.2378&json=true","fetch_graph":"https://pith.science/api/pith-number/4I2UN5ZRL3SZOZPZD6J6CJEMUB/graph.json","fetch_events":"https://pith.science/api/pith-number/4I2UN5ZRL3SZOZPZD6J6CJEMUB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB/action/storage_attestation","attest_author":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB/action/author_attestation","sign_citation":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB/action/citation_signature","submit_replication":"https://pith.science/pith/4I2UN5ZRL3SZOZPZD6J6CJEMUB/action/replication_record"}},"created_at":"2026-05-18T04:13:37.819696+00:00","updated_at":"2026-05-18T04:13:37.819696+00:00"}