{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6CKFVIG5SGYYY2WO3YK23PSOQO","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":"1ea91cf83ae6f3315d0d2b08e668721020685737e6a5b01052ce0dac5e710ff7","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-29T13:48:33Z","title_canon_sha256":"1aa9dcb600182a5e82ac423855e7be82e81eb10e6e342b48878b1ce81c612e35"},"schema_version":"1.0","source":{"id":"2509.25289","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.25289","created_at":"2026-06-04T01:09:38Z"},{"alias_kind":"arxiv_version","alias_value":"2509.25289v4","created_at":"2026-06-04T01:09:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.25289","created_at":"2026-06-04T01:09:38Z"},{"alias_kind":"pith_short_12","alias_value":"6CKFVIG5SGYY","created_at":"2026-06-04T01:09:38Z"},{"alias_kind":"pith_short_16","alias_value":"6CKFVIG5SGYYY2WO","created_at":"2026-06-04T01:09:38Z"},{"alias_kind":"pith_short_8","alias_value":"6CKFVIG5","created_at":"2026-06-04T01:09:38Z"}],"graph_snapshots":[{"event_id":"sha256:af57d4af5931279dcc19bd476066e226b539ee7aa07be493630c854e2a3a157c","target":"graph","created_at":"2026-06-04T01:09:38Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.25289/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue. We introduce ClustRecNet, a novel end-to-end deep learning framework that recommends suitable clustering algorithm(s) by directly learning high-order representations of raw tabular data. To facilitate robust meta-learning, we first construct a comprehensive repository of 34,000 synthetic datasets encompassing a large variety of clustering scenarios, run 10 popular clustering algorithms, and use Adjusted Rand Index (ARI) to establish ground-truth labels. ClustRecNet's architectur","authors_text":"Bogdan Mazoure, Guillaume Rabusseau, Mohammadreza Bakhtyari, Renato Cordeiro de Amorim, Vladimir Makarenkov","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-29T13:48:33Z","title":"ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.25289","kind":"arxiv","version":4},"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:c1fa03ba479be14d0d343e0b01573e42c5501725e9838214e0b353c937fb0e68","target":"record","created_at":"2026-06-04T01:09:38Z","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":"1ea91cf83ae6f3315d0d2b08e668721020685737e6a5b01052ce0dac5e710ff7","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-29T13:48:33Z","title_canon_sha256":"1aa9dcb600182a5e82ac423855e7be82e81eb10e6e342b48878b1ce81c612e35"},"schema_version":"1.0","source":{"id":"2509.25289","kind":"arxiv","version":4}},"canonical_sha256":"f0945aa0dd91b18c6acede15adbe4e838480e57caaf15e16750e6d264394cf98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f0945aa0dd91b18c6acede15adbe4e838480e57caaf15e16750e6d264394cf98","first_computed_at":"2026-06-04T01:09:38.619357Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:09:38.619357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+g+UJkAxwhjg5iI2Cb6ujlNE5OUZ7a2DhM9g9DmqC5i7iy9Y0DZp+cK2u42wcPXBFO76Hgm5YHF1czbX/YNaCQ==","signature_status":"signed_v1","signed_at":"2026-06-04T01:09:38.619976Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.25289","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1fa03ba479be14d0d343e0b01573e42c5501725e9838214e0b353c937fb0e68","sha256:af57d4af5931279dcc19bd476066e226b539ee7aa07be493630c854e2a3a157c"],"state_sha256":"473f9c81441d3051e162586af88f42b889bc4741414611bf29843987531ffbbd"}