{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6PMOZBVAZ5DZMTQMLYTEMJAMAW","short_pith_number":"pith:6PMOZBVA","schema_version":"1.0","canonical_sha256":"f3d8ec86a0cf47964e0c5e2646240c05b646b10a980ac73df37757c034b5473c","source":{"kind":"arxiv","id":"2207.04649","version":4},"attestation_state":"computed","paper":{"title":"Fast Density-Peaks Clustering: Multicore-based Parallelization Approach","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Daichi Amagata, Takahiro Hara","submitted_at":"2022-07-11T06:19:15Z","abstract_excerpt":"Clustering multi-dimensional points is a fundamental task in many fields, and density-based clustering supports many applications as it can discover clusters of arbitrary shapes. This paper addresses the problem of Density-Peaks Clustering (DPC), a recently proposed density-based clustering framework. Although DPC already has many applications, its straightforward implementation incurs a quadratic time computation to the number of points in a given dataset, thereby does not scale to large datasets. To enable DPC on large datasets, we propose efficient algorithms for DPC. Specifically, we propo"},"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":"2207.04649","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DB","submitted_at":"2022-07-11T06:19:15Z","cross_cats_sorted":[],"title_canon_sha256":"9de778e1a81cb24c6a4e9be4a586a0fbb460979f1f721e9657bd1638fafbc315","abstract_canon_sha256":"8c297f54f66a3873ce17708ebd4db07246b757e30abe3770fec56eba5912cbb6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:21:02.170345Z","signature_b64":"hc9AKvI4N29HCCUWkli0QpE2h96ma2fgk05Hzxs/TPWlIdjnSKYeafTA8PLi0Lu2elx8UOLv9NBjgDEhNpR1Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f3d8ec86a0cf47964e0c5e2646240c05b646b10a980ac73df37757c034b5473c","last_reissued_at":"2026-07-05T05:21:02.169911Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:21:02.169911Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Density-Peaks Clustering: Multicore-based Parallelization Approach","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Daichi Amagata, Takahiro Hara","submitted_at":"2022-07-11T06:19:15Z","abstract_excerpt":"Clustering multi-dimensional points is a fundamental task in many fields, and density-based clustering supports many applications as it can discover clusters of arbitrary shapes. This paper addresses the problem of Density-Peaks Clustering (DPC), a recently proposed density-based clustering framework. Although DPC already has many applications, its straightforward implementation incurs a quadratic time computation to the number of points in a given dataset, thereby does not scale to large datasets. To enable DPC on large datasets, we propose efficient algorithms for DPC. Specifically, we propo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.04649","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2207.04649/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2207.04649","created_at":"2026-07-05T05:21:02.169966+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.04649v4","created_at":"2026-07-05T05:21:02.169966+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.04649","created_at":"2026-07-05T05:21:02.169966+00:00"},{"alias_kind":"pith_short_12","alias_value":"6PMOZBVAZ5DZ","created_at":"2026-07-05T05:21:02.169966+00:00"},{"alias_kind":"pith_short_16","alias_value":"6PMOZBVAZ5DZMTQM","created_at":"2026-07-05T05:21:02.169966+00:00"},{"alias_kind":"pith_short_8","alias_value":"6PMOZBVA","created_at":"2026-07-05T05:21:02.169966+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW","json":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW.json","graph_json":"https://pith.science/api/pith-number/6PMOZBVAZ5DZMTQMLYTEMJAMAW/graph.json","events_json":"https://pith.science/api/pith-number/6PMOZBVAZ5DZMTQMLYTEMJAMAW/events.json","paper":"https://pith.science/paper/6PMOZBVA"},"agent_actions":{"view_html":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW","download_json":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW.json","view_paper":"https://pith.science/paper/6PMOZBVA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.04649&json=true","fetch_graph":"https://pith.science/api/pith-number/6PMOZBVAZ5DZMTQMLYTEMJAMAW/graph.json","fetch_events":"https://pith.science/api/pith-number/6PMOZBVAZ5DZMTQMLYTEMJAMAW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW/action/storage_attestation","attest_author":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW/action/author_attestation","sign_citation":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW/action/citation_signature","submit_replication":"https://pith.science/pith/6PMOZBVAZ5DZMTQMLYTEMJAMAW/action/replication_record"}},"created_at":"2026-07-05T05:21:02.169966+00:00","updated_at":"2026-07-05T05:21:02.169966+00:00"}