{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:YHVFGYWQHUBYQS7MFF53NVP4ZK","short_pith_number":"pith:YHVFGYWQ","schema_version":"1.0","canonical_sha256":"c1ea5362d03d03884bec297bb6d5fccaaf686d0d5934e7f62075850b00822c28","source":{"kind":"arxiv","id":"1409.4988","version":1},"attestation_state":"computed","paper":{"title":"An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.MA"],"primary_cat":"cs.LG","authors_text":"Alireza Sadeghian, Antonello Rizzi, Enrico Maiorino, Filippo Maria Bianchi, Lorenzo Livi","submitted_at":"2014-09-17T14:39:37Z","abstract_excerpt":"We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a suitable weighted graph representation of the input dataset. Such a weighted graph representation is induced by the specific parameter configuration of the dissimilarity measure adopted by the agent, which searches and takes decisions autonomously for one cluster at a ti"},"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":"1409.4988","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-09-17T14:39:37Z","cross_cats_sorted":["cs.DC","cs.MA"],"title_canon_sha256":"6108b84ddbdf63af1fa5708936b3226d5922b0ea946d4c4210fd4ee4410d03cc","abstract_canon_sha256":"70504afc8b07d4e4e02f0bd2d006ade7a0754eaf98965596229b3605b58542e7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:33:16.489808Z","signature_b64":"TGcbs8CaheCCnZ+CsaOIolgko6eOSy7CRSy+vcKwYwp1BvpThl3QTE+7eTg6sTF5ce7jhf+/dZMsvNAgrF3EAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1ea5362d03d03884bec297bb6d5fccaaf686d0d5934e7f62075850b00822c28","last_reissued_at":"2026-05-18T01:33:16.489090Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:33:16.489090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.MA"],"primary_cat":"cs.LG","authors_text":"Alireza Sadeghian, Antonello Rizzi, Enrico Maiorino, Filippo Maria Bianchi, Lorenzo Livi","submitted_at":"2014-09-17T14:39:37Z","abstract_excerpt":"We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a suitable weighted graph representation of the input dataset. Such a weighted graph representation is induced by the specific parameter configuration of the dissimilarity measure adopted by the agent, which searches and takes decisions autonomously for one cluster at a ti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.4988","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":"1409.4988","created_at":"2026-05-18T01:33:16.489207+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.4988v1","created_at":"2026-05-18T01:33:16.489207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.4988","created_at":"2026-05-18T01:33:16.489207+00:00"},{"alias_kind":"pith_short_12","alias_value":"YHVFGYWQHUBY","created_at":"2026-05-18T12:28:57.508820+00:00"},{"alias_kind":"pith_short_16","alias_value":"YHVFGYWQHUBYQS7M","created_at":"2026-05-18T12:28:57.508820+00:00"},{"alias_kind":"pith_short_8","alias_value":"YHVFGYWQ","created_at":"2026-05-18T12:28:57.508820+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/YHVFGYWQHUBYQS7MFF53NVP4ZK","json":"https://pith.science/pith/YHVFGYWQHUBYQS7MFF53NVP4ZK.json","graph_json":"https://pith.science/api/pith-number/YHVFGYWQHUBYQS7MFF53NVP4ZK/graph.json","events_json":"https://pith.science/api/pith-number/YHVFGYWQHUBYQS7MFF53NVP4ZK/events.json","paper":"https://pith.science/paper/YHVFGYWQ"},"agent_actions":{"view_html":"https://pith.science/pith/YHVFGYWQHUBYQS7MFF53NVP4ZK","download_json":"https://pith.science/pith/YHVFGYWQHUBYQS7MFF53NVP4ZK.json","view_paper":"https://pith.science/paper/YHVFGYWQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.4988&json=true","fetch_graph":"https://pith.science/api/pith-number/YHVFGYWQHUBYQS7MFF53NVP4ZK/graph.json","fetch_events":"https://pith.science/api/pith-number/YHVFGYWQHUBYQS7MFF53NVP4ZK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YHVFGYWQHUBYQS7MFF53NVP4ZK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YHVFGYWQHUBYQS7MFF53NVP4ZK/action/storage_attestation","attest_author":"https://pith.science/pith/YHVFGYWQHUBYQS7MFF53NVP4ZK/action/author_attestation","sign_citation":"https://pith.science/pith/YHVFGYWQHUBYQS7MFF53NVP4ZK/action/citation_signature","submit_replication":"https://pith.science/pith/YHVFGYWQHUBYQS7MFF53NVP4ZK/action/replication_record"}},"created_at":"2026-05-18T01:33:16.489207+00:00","updated_at":"2026-05-18T01:33:16.489207+00:00"}