{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:EOZR5XXRM5KEPYRCCN7WUT2HYC","short_pith_number":"pith:EOZR5XXR","canonical_record":{"source":{"id":"1904.04020","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-08T12:49:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0d67881d3f996d34818c2085b5148ab6fe38ee33556c7d7b77b5de4c88b53b11","abstract_canon_sha256":"971b03ceacd37ebecb20f46fc16ee32e4d1998bcbfb151d96022120490c07ae5"},"schema_version":"1.0"},"canonical_sha256":"23b31edef1675447e222137f6a4f47c08b319850eb22a8e0dd4c943b64af13ce","source":{"kind":"arxiv","id":"1904.04020","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.04020","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"arxiv_version","alias_value":"1904.04020v1","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04020","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"pith_short_12","alias_value":"EOZR5XXRM5KE","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"EOZR5XXRM5KEPYRC","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"EOZR5XXR","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:EOZR5XXRM5KEPYRCCN7WUT2HYC","target":"record","payload":{"canonical_record":{"source":{"id":"1904.04020","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-08T12:49:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0d67881d3f996d34818c2085b5148ab6fe38ee33556c7d7b77b5de4c88b53b11","abstract_canon_sha256":"971b03ceacd37ebecb20f46fc16ee32e4d1998bcbfb151d96022120490c07ae5"},"schema_version":"1.0"},"canonical_sha256":"23b31edef1675447e222137f6a4f47c08b319850eb22a8e0dd4c943b64af13ce","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:07.343726Z","signature_b64":"0XoAXmyi1geSNVA6FQLgHOkLu9XLX5WqYitZWEiygRXgRdAeC+bRmtkjfpCfaEWjtNI6cji9vL8TD8VYzhijCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23b31edef1675447e222137f6a4f47c08b319850eb22a8e0dd4c943b64af13ce","last_reissued_at":"2026-05-17T23:49:07.343098Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:07.343098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.04020","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K5zLo+5dkOixycboNs2QYFrzwe8tfrRTN7yq/3rJuxTPeAGsS1kLNMvW4a6jXKchO2sbDhGaI0UzPB0TcWBpAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T00:24:00.771741Z"},"content_sha256":"6fa62ce9c72ff92f10be86d26c930c6a18fcb9afd081d1e43597c65fbda4351b","schema_version":"1.0","event_id":"sha256:6fa62ce9c72ff92f10be86d26c930c6a18fcb9afd081d1e43597c65fbda4351b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:EOZR5XXRM5KEPYRCCN7WUT2HYC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CRAD: Clustering with Robust Autocuts and Depth","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Xin Huang, Yulia R. Gel","submitted_at":"2019-04-08T12:49:45Z","abstract_excerpt":"We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure. Our experiments prove that the new CRAD is highly competitive at detecting clusters with varying densities, compared with the existing algorithms such as DBSCAN, OPTICS and DBCA. Furthermore, a new effective parameter selection procedure is developed to select the optimal underlying parameter in the real-world clustering, when the ground truth is unknown. Lastly, we suggest a new clustering framework that extends CRAD from spa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04020","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mZZT5323p7AxciT4sB7uCXmXib0Hmpfx0BP78muf7EmxnPPq5JDaoyo76L8U/VYPTNFnsoQj9AReFO+AKXR9AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T00:24:00.772098Z"},"content_sha256":"d1016e51180308f5834da3a31ab9411583df6030d3405eaf3bcfff305ed1e0b9","schema_version":"1.0","event_id":"sha256:d1016e51180308f5834da3a31ab9411583df6030d3405eaf3bcfff305ed1e0b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EOZR5XXRM5KEPYRCCN7WUT2HYC/bundle.json","state_url":"https://pith.science/pith/EOZR5XXRM5KEPYRCCN7WUT2HYC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EOZR5XXRM5KEPYRCCN7WUT2HYC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-03T00:24:00Z","links":{"resolver":"https://pith.science/pith/EOZR5XXRM5KEPYRCCN7WUT2HYC","bundle":"https://pith.science/pith/EOZR5XXRM5KEPYRCCN7WUT2HYC/bundle.json","state":"https://pith.science/pith/EOZR5XXRM5KEPYRCCN7WUT2HYC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EOZR5XXRM5KEPYRCCN7WUT2HYC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:EOZR5XXRM5KEPYRCCN7WUT2HYC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"971b03ceacd37ebecb20f46fc16ee32e4d1998bcbfb151d96022120490c07ae5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-08T12:49:45Z","title_canon_sha256":"0d67881d3f996d34818c2085b5148ab6fe38ee33556c7d7b77b5de4c88b53b11"},"schema_version":"1.0","source":{"id":"1904.04020","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.04020","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"arxiv_version","alias_value":"1904.04020v1","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04020","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"pith_short_12","alias_value":"EOZR5XXRM5KE","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"EOZR5XXRM5KEPYRC","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"EOZR5XXR","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:d1016e51180308f5834da3a31ab9411583df6030d3405eaf3bcfff305ed1e0b9","target":"graph","created_at":"2026-05-17T23:49:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure. Our experiments prove that the new CRAD is highly competitive at detecting clusters with varying densities, compared with the existing algorithms such as DBSCAN, OPTICS and DBCA. Furthermore, a new effective parameter selection procedure is developed to select the optimal underlying parameter in the real-world clustering, when the ground truth is unknown. Lastly, we suggest a new clustering framework that extends CRAD from spa","authors_text":"Xin Huang, Yulia R. Gel","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-08T12:49:45Z","title":"CRAD: Clustering with Robust Autocuts and Depth"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04020","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6fa62ce9c72ff92f10be86d26c930c6a18fcb9afd081d1e43597c65fbda4351b","target":"record","created_at":"2026-05-17T23:49:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"971b03ceacd37ebecb20f46fc16ee32e4d1998bcbfb151d96022120490c07ae5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-04-08T12:49:45Z","title_canon_sha256":"0d67881d3f996d34818c2085b5148ab6fe38ee33556c7d7b77b5de4c88b53b11"},"schema_version":"1.0","source":{"id":"1904.04020","kind":"arxiv","version":1}},"canonical_sha256":"23b31edef1675447e222137f6a4f47c08b319850eb22a8e0dd4c943b64af13ce","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23b31edef1675447e222137f6a4f47c08b319850eb22a8e0dd4c943b64af13ce","first_computed_at":"2026-05-17T23:49:07.343098Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:07.343098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0XoAXmyi1geSNVA6FQLgHOkLu9XLX5WqYitZWEiygRXgRdAeC+bRmtkjfpCfaEWjtNI6cji9vL8TD8VYzhijCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:07.343726Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.04020","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6fa62ce9c72ff92f10be86d26c930c6a18fcb9afd081d1e43597c65fbda4351b","sha256:d1016e51180308f5834da3a31ab9411583df6030d3405eaf3bcfff305ed1e0b9"],"state_sha256":"fc629b09d0c4e1c3571fff00964565bec7949e3e39d7326f67aa162fcbd75b0c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2QetoPlMCbgiXjmMMk8sTzmIvJ+IBE2zWAKfsFT2tDQmTtOHLeq4xm/BpVmeHzBoxF1HZPDwP3zGMcYJwXaRAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T00:24:00.774159Z","bundle_sha256":"d6220c6896aaf0a4c73e39a0eaf8c489fb9dc29b29868f5cbd513cf4fa8e48e3"}}