{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:ZNV6GRVX656JZT67DZFK6EIH6C","short_pith_number":"pith:ZNV6GRVX","schema_version":"1.0","canonical_sha256":"cb6be346b7f77c9ccfdf1e4aaf1107f0949963837f70b2ccaba735b63f3399ae","source":{"kind":"arxiv","id":"1512.02097","version":1},"attestation_state":"computed","paper":{"title":"Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.CO","stat.ME"],"primary_cat":"stat.ML","authors_text":"Teng Qiu, Yongjie Li","submitted_at":"2015-12-07T15:47:49Z","abstract_excerpt":"Most density-based clustering methods largely rely on how well the underlying density is estimated. However, density estimation itself is also a challenging problem, especially the determination of the kernel bandwidth. A large bandwidth could lead to the over-smoothed density estimation in which the number of density peaks could be less than the true clusters, while a small bandwidth could lead to the under-smoothed density estimation in which spurious density peaks, or called the \"ripple noise\", would be generated in the estimated density. In this paper, we propose a density-based hierarchic"},"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":"1512.02097","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2015-12-07T15:47:49Z","cross_cats_sorted":["cs.CV","cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"045b516de9ceda0ef74ad9363084a6423f7739fe8ba1215dbe0d704de2aff06b","abstract_canon_sha256":"c57e473d72f217a8a57e195932787a5df2e061d3d3e0698c1d4d2fedda2a3039"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:25:09.628815Z","signature_b64":"CWRQ9X2IDxavyNl9yzhtWxtFpXjvzlQFo7S1B1AA4bSwRWKNKxdlQ+W2WIKf1HGwr9XB0IB9JDP0A5lSnyUmDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb6be346b7f77c9ccfdf1e4aaf1107f0949963837f70b2ccaba735b63f3399ae","last_reissued_at":"2026-05-18T01:25:09.628249Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:25:09.628249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Clustering by Deep Nearest Neighbor Descent (D-NND): A Density-based Parameter-Insensitive Clustering Method","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.CO","stat.ME"],"primary_cat":"stat.ML","authors_text":"Teng Qiu, Yongjie Li","submitted_at":"2015-12-07T15:47:49Z","abstract_excerpt":"Most density-based clustering methods largely rely on how well the underlying density is estimated. However, density estimation itself is also a challenging problem, especially the determination of the kernel bandwidth. A large bandwidth could lead to the over-smoothed density estimation in which the number of density peaks could be less than the true clusters, while a small bandwidth could lead to the under-smoothed density estimation in which spurious density peaks, or called the \"ripple noise\", would be generated in the estimated density. In this paper, we propose a density-based hierarchic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.02097","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":"1512.02097","created_at":"2026-05-18T01:25:09.628357+00:00"},{"alias_kind":"arxiv_version","alias_value":"1512.02097v1","created_at":"2026-05-18T01:25:09.628357+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.02097","created_at":"2026-05-18T01:25:09.628357+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZNV6GRVX656J","created_at":"2026-05-18T12:29:52.810259+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZNV6GRVX656JZT67","created_at":"2026-05-18T12:29:52.810259+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZNV6GRVX","created_at":"2026-05-18T12:29:52.810259+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/ZNV6GRVX656JZT67DZFK6EIH6C","json":"https://pith.science/pith/ZNV6GRVX656JZT67DZFK6EIH6C.json","graph_json":"https://pith.science/api/pith-number/ZNV6GRVX656JZT67DZFK6EIH6C/graph.json","events_json":"https://pith.science/api/pith-number/ZNV6GRVX656JZT67DZFK6EIH6C/events.json","paper":"https://pith.science/paper/ZNV6GRVX"},"agent_actions":{"view_html":"https://pith.science/pith/ZNV6GRVX656JZT67DZFK6EIH6C","download_json":"https://pith.science/pith/ZNV6GRVX656JZT67DZFK6EIH6C.json","view_paper":"https://pith.science/paper/ZNV6GRVX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1512.02097&json=true","fetch_graph":"https://pith.science/api/pith-number/ZNV6GRVX656JZT67DZFK6EIH6C/graph.json","fetch_events":"https://pith.science/api/pith-number/ZNV6GRVX656JZT67DZFK6EIH6C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZNV6GRVX656JZT67DZFK6EIH6C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZNV6GRVX656JZT67DZFK6EIH6C/action/storage_attestation","attest_author":"https://pith.science/pith/ZNV6GRVX656JZT67DZFK6EIH6C/action/author_attestation","sign_citation":"https://pith.science/pith/ZNV6GRVX656JZT67DZFK6EIH6C/action/citation_signature","submit_replication":"https://pith.science/pith/ZNV6GRVX656JZT67DZFK6EIH6C/action/replication_record"}},"created_at":"2026-05-18T01:25:09.628357+00:00","updated_at":"2026-05-18T01:25:09.628357+00:00"}