{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:QWSBAK44VXNAQK5TN3QZHQBG7P","short_pith_number":"pith:QWSBAK44","canonical_record":{"source":{"id":"1512.03953","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-12T19:33:52Z","cross_cats_sorted":[],"title_canon_sha256":"b06d06a3962be19bc84a64bbaab6e188f25dde1c033b6dea4d8b6289dd33ee35","abstract_canon_sha256":"8d157efae3029350603fa8b986c3db19e0c785fc9bde0aff39faf3547025f15b"},"schema_version":"1.0"},"canonical_sha256":"85a4102b9cadda082bb36ee193c026fbd81bc7e5915ec582e39964e4a2edbdd4","source":{"kind":"arxiv","id":"1512.03953","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.03953","created_at":"2026-05-18T01:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"1512.03953v1","created_at":"2026-05-18T01:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.03953","created_at":"2026-05-18T01:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"QWSBAK44VXNA","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"QWSBAK44VXNAQK5T","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"QWSBAK44","created_at":"2026-05-18T12:29:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:QWSBAK44VXNAQK5TN3QZHQBG7P","target":"record","payload":{"canonical_record":{"source":{"id":"1512.03953","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-12T19:33:52Z","cross_cats_sorted":[],"title_canon_sha256":"b06d06a3962be19bc84a64bbaab6e188f25dde1c033b6dea4d8b6289dd33ee35","abstract_canon_sha256":"8d157efae3029350603fa8b986c3db19e0c785fc9bde0aff39faf3547025f15b"},"schema_version":"1.0"},"canonical_sha256":"85a4102b9cadda082bb36ee193c026fbd81bc7e5915ec582e39964e4a2edbdd4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:24:23.485747Z","signature_b64":"PbcTEtK0O0scXKxE9RApAe2j/CThN88GeTzuk6HSLu32tuYyX1nyy0lSo1DYaW5oRDz7Mevhje+ssntzuJ49Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85a4102b9cadda082bb36ee193c026fbd81bc7e5915ec582e39964e4a2edbdd4","last_reissued_at":"2026-05-18T01:24:23.485112Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:24:23.485112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1512.03953","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":"2026-05-18T01:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W1zJ6vvR1MftbdvIwb0+Zq1Z0101I5a56f8JVpyHVIwWdEZCbnsh+8UocPnzFbmCFQna/N+k7VVccCpkr16JAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T15:52:48.955944Z"},"content_sha256":"2bd265f83b365c3b9485f59b0ff5c1031f3afe240b1e68f7cf58725ddddae444","schema_version":"1.0","event_id":"sha256:2bd265f83b365c3b9485f59b0ff5c1031f3afe240b1e68f7cf58725ddddae444"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:QWSBAK44VXNAQK5TN3QZHQBG7P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Active Distance-Based Clustering using K-medoids","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amin Aghaee, Mahdieh Soleymani Baghshah, Mehrdad Ghadiri","submitted_at":"2015-12-12T19:33:52Z","abstract_excerpt":"k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with $n$ points into a set of $k$ disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a small proportion of the whole set of distances and makes an effort to estimate an upper-bound for unknown distances using the inquired ones. This algorithm makes use of the triangle inequality to calcul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.03953","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":"2026-05-18T01:24:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TzwHwGM0SVYFfDtSfDWGKif6ire6DRmzs1DonCEsKy4fXINCAmV8Z8z1Tv2ZOOahj2oqf3ZjCtR7yXjgE8MrBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T15:52:48.956276Z"},"content_sha256":"4669eb1f9c33d15396e80d135bfdac5c02fdb3744930993d0c902a4a7717a271","schema_version":"1.0","event_id":"sha256:4669eb1f9c33d15396e80d135bfdac5c02fdb3744930993d0c902a4a7717a271"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QWSBAK44VXNAQK5TN3QZHQBG7P/bundle.json","state_url":"https://pith.science/pith/QWSBAK44VXNAQK5TN3QZHQBG7P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QWSBAK44VXNAQK5TN3QZHQBG7P/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":"2026-06-29T15:52:48Z","links":{"resolver":"https://pith.science/pith/QWSBAK44VXNAQK5TN3QZHQBG7P","bundle":"https://pith.science/pith/QWSBAK44VXNAQK5TN3QZHQBG7P/bundle.json","state":"https://pith.science/pith/QWSBAK44VXNAQK5TN3QZHQBG7P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QWSBAK44VXNAQK5TN3QZHQBG7P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:QWSBAK44VXNAQK5TN3QZHQBG7P","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":"8d157efae3029350603fa8b986c3db19e0c785fc9bde0aff39faf3547025f15b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-12T19:33:52Z","title_canon_sha256":"b06d06a3962be19bc84a64bbaab6e188f25dde1c033b6dea4d8b6289dd33ee35"},"schema_version":"1.0","source":{"id":"1512.03953","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.03953","created_at":"2026-05-18T01:24:23Z"},{"alias_kind":"arxiv_version","alias_value":"1512.03953v1","created_at":"2026-05-18T01:24:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.03953","created_at":"2026-05-18T01:24:23Z"},{"alias_kind":"pith_short_12","alias_value":"QWSBAK44VXNA","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_16","alias_value":"QWSBAK44VXNAQK5T","created_at":"2026-05-18T12:29:39Z"},{"alias_kind":"pith_short_8","alias_value":"QWSBAK44","created_at":"2026-05-18T12:29:39Z"}],"graph_snapshots":[{"event_id":"sha256:4669eb1f9c33d15396e80d135bfdac5c02fdb3744930993d0c902a4a7717a271","target":"graph","created_at":"2026-05-18T01:24:23Z","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":"k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with $n$ points into a set of $k$ disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a small proportion of the whole set of distances and makes an effort to estimate an upper-bound for unknown distances using the inquired ones. This algorithm makes use of the triangle inequality to calcul","authors_text":"Amin Aghaee, Mahdieh Soleymani Baghshah, Mehrdad Ghadiri","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-12T19:33:52Z","title":"Active Distance-Based Clustering using K-medoids"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.03953","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":"sha256:2bd265f83b365c3b9485f59b0ff5c1031f3afe240b1e68f7cf58725ddddae444","target":"record","created_at":"2026-05-18T01:24:23Z","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":"8d157efae3029350603fa8b986c3db19e0c785fc9bde0aff39faf3547025f15b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-12-12T19:33:52Z","title_canon_sha256":"b06d06a3962be19bc84a64bbaab6e188f25dde1c033b6dea4d8b6289dd33ee35"},"schema_version":"1.0","source":{"id":"1512.03953","kind":"arxiv","version":1}},"canonical_sha256":"85a4102b9cadda082bb36ee193c026fbd81bc7e5915ec582e39964e4a2edbdd4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85a4102b9cadda082bb36ee193c026fbd81bc7e5915ec582e39964e4a2edbdd4","first_computed_at":"2026-05-18T01:24:23.485112Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:24:23.485112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PbcTEtK0O0scXKxE9RApAe2j/CThN88GeTzuk6HSLu32tuYyX1nyy0lSo1DYaW5oRDz7Mevhje+ssntzuJ49Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:24:23.485747Z","signed_message":"canonical_sha256_bytes"},"source_id":"1512.03953","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2bd265f83b365c3b9485f59b0ff5c1031f3afe240b1e68f7cf58725ddddae444","sha256:4669eb1f9c33d15396e80d135bfdac5c02fdb3744930993d0c902a4a7717a271"],"state_sha256":"112ffd6855d791feb53ff9373181fe75e326a38c13415f9dab234af686c09e89"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BAiXVetmLawNZw3bYp66VuizJh3NcXMUK14yMkhHBFZ+b44lBUulj2I7g94eOVtCYZ6V9PWqPZ1iCykseiOSAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T15:52:48.958165Z","bundle_sha256":"9ca2fdf59cb81a0748072ab869111f8ddb517c0992136d24edb40203e296488c"}}