{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:22XPIUEIOYY4Q3EY6RSQBB2OZU","short_pith_number":"pith:22XPIUEI","schema_version":"1.0","canonical_sha256":"d6aef450887631c86c98f46500874ecd38f2bd5d8ddfe784b0985b975ede151c","source":{"kind":"arxiv","id":"1703.02883","version":1},"attestation_state":"computed","paper":{"title":"Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Hadi Veisi, Hadi Zare, Hossein Bobarshad, Kayvan Bijari","submitted_at":"2017-03-08T15:50:35Z","abstract_excerpt":"Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang-big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory based scheme as compared to its similar "},"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":"1703.02883","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-03-08T15:50:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c43e7e0530d849c4c84cf53cd53483260cc034eb4061bd4b0e891d7cb4733b32","abstract_canon_sha256":"70a30411e991484f73746e44cc673b3cb69078494c70aba44e3cac1db983e4ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:05.411241Z","signature_b64":"db0Ppl2c311R975yzsrQjsmIortkFL0HusTo3nWnjceLa3MnMA0nOpHKbBTDTG0Hcbme+e1vn2gpQ3NlBEqlDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6aef450887631c86c98f46500874ecd38f2bd5d8ddfe784b0985b975ede151c","last_reissued_at":"2026-05-18T00:49:05.410694Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:05.410694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Hadi Veisi, Hadi Zare, Hossein Bobarshad, Kayvan Bijari","submitted_at":"2017-03-08T15:50:35Z","abstract_excerpt":"Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approach based on big bang-big crunch algorithm is proposed for clustering problems. The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory based scheme as compared to its similar "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02883","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":"1703.02883","created_at":"2026-05-18T00:49:05.410787+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.02883v1","created_at":"2026-05-18T00:49:05.410787+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02883","created_at":"2026-05-18T00:49:05.410787+00:00"},{"alias_kind":"pith_short_12","alias_value":"22XPIUEIOYY4","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"22XPIUEIOYY4Q3EY","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"22XPIUEI","created_at":"2026-05-18T12:30:55.937587+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/22XPIUEIOYY4Q3EY6RSQBB2OZU","json":"https://pith.science/pith/22XPIUEIOYY4Q3EY6RSQBB2OZU.json","graph_json":"https://pith.science/api/pith-number/22XPIUEIOYY4Q3EY6RSQBB2OZU/graph.json","events_json":"https://pith.science/api/pith-number/22XPIUEIOYY4Q3EY6RSQBB2OZU/events.json","paper":"https://pith.science/paper/22XPIUEI"},"agent_actions":{"view_html":"https://pith.science/pith/22XPIUEIOYY4Q3EY6RSQBB2OZU","download_json":"https://pith.science/pith/22XPIUEIOYY4Q3EY6RSQBB2OZU.json","view_paper":"https://pith.science/paper/22XPIUEI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.02883&json=true","fetch_graph":"https://pith.science/api/pith-number/22XPIUEIOYY4Q3EY6RSQBB2OZU/graph.json","fetch_events":"https://pith.science/api/pith-number/22XPIUEIOYY4Q3EY6RSQBB2OZU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/22XPIUEIOYY4Q3EY6RSQBB2OZU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/22XPIUEIOYY4Q3EY6RSQBB2OZU/action/storage_attestation","attest_author":"https://pith.science/pith/22XPIUEIOYY4Q3EY6RSQBB2OZU/action/author_attestation","sign_citation":"https://pith.science/pith/22XPIUEIOYY4Q3EY6RSQBB2OZU/action/citation_signature","submit_replication":"https://pith.science/pith/22XPIUEIOYY4Q3EY6RSQBB2OZU/action/replication_record"}},"created_at":"2026-05-18T00:49:05.410787+00:00","updated_at":"2026-05-18T00:49:05.410787+00:00"}