{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WB4WA7DS4MUXAVIBRF6ZFAHGZ7","short_pith_number":"pith:WB4WA7DS","schema_version":"1.0","canonical_sha256":"b079607c72e329705501897d9280e6cfc0c947767e5812f2a59243f75f032de9","source":{"kind":"arxiv","id":"1807.09250","version":1},"attestation_state":"computed","paper":{"title":"Using Multi-Core HW/SW Co-design Architecture for Accelerating K-means Clustering Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.DC","authors_text":"Hadi Mardani Kamali","submitted_at":"2018-07-09T17:38:42Z","abstract_excerpt":"The capability of classifying and clustering a desired set of data is an essential part of building knowledge from data. However, as the size and dimensionality of input data increases, the run-time for such clustering algorithms is expected to grow superlinearly, making it a big challenge when dealing with BigData. K-mean clustering is an essential tool for many big data applications including data mining, predictive analysis, forecasting studies, and machine learning. However, due to large size (volume) of Big-Data, and large dimensionality of its data points, even the application of a simpl"},"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":"1807.09250","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DC","submitted_at":"2018-07-09T17:38:42Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"cbb9eb684e77d5acba4f433c3bb995d847c8fd72671d3962557d220a8b3f32eb","abstract_canon_sha256":"3f1989090f5d67866e21aa730d726be40b2023f21d13153af6c4903b2915484a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:56.288812Z","signature_b64":"Sk1x1VPTzG/YJ3NdH5sw4bIna5ZbCk3JB5MsnO7lP4q0ihgwKJk9hD8K09TDY4gPkp6SjoSDhLaE79pSmAMnCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b079607c72e329705501897d9280e6cfc0c947767e5812f2a59243f75f032de9","last_reissued_at":"2026-05-18T00:09:56.288084Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:56.288084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Multi-Core HW/SW Co-design Architecture for Accelerating K-means Clustering Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.DC","authors_text":"Hadi Mardani Kamali","submitted_at":"2018-07-09T17:38:42Z","abstract_excerpt":"The capability of classifying and clustering a desired set of data is an essential part of building knowledge from data. However, as the size and dimensionality of input data increases, the run-time for such clustering algorithms is expected to grow superlinearly, making it a big challenge when dealing with BigData. K-mean clustering is an essential tool for many big data applications including data mining, predictive analysis, forecasting studies, and machine learning. However, due to large size (volume) of Big-Data, and large dimensionality of its data points, even the application of a simpl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09250","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":"1807.09250","created_at":"2026-05-18T00:09:56.288199+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.09250v1","created_at":"2026-05-18T00:09:56.288199+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09250","created_at":"2026-05-18T00:09:56.288199+00:00"},{"alias_kind":"pith_short_12","alias_value":"WB4WA7DS4MUX","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"WB4WA7DS4MUXAVIB","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"WB4WA7DS","created_at":"2026-05-18T12:32:59.047623+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/WB4WA7DS4MUXAVIBRF6ZFAHGZ7","json":"https://pith.science/pith/WB4WA7DS4MUXAVIBRF6ZFAHGZ7.json","graph_json":"https://pith.science/api/pith-number/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/graph.json","events_json":"https://pith.science/api/pith-number/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/events.json","paper":"https://pith.science/paper/WB4WA7DS"},"agent_actions":{"view_html":"https://pith.science/pith/WB4WA7DS4MUXAVIBRF6ZFAHGZ7","download_json":"https://pith.science/pith/WB4WA7DS4MUXAVIBRF6ZFAHGZ7.json","view_paper":"https://pith.science/paper/WB4WA7DS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.09250&json=true","fetch_graph":"https://pith.science/api/pith-number/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/graph.json","fetch_events":"https://pith.science/api/pith-number/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/action/storage_attestation","attest_author":"https://pith.science/pith/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/action/author_attestation","sign_citation":"https://pith.science/pith/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/action/citation_signature","submit_replication":"https://pith.science/pith/WB4WA7DS4MUXAVIBRF6ZFAHGZ7/action/replication_record"}},"created_at":"2026-05-18T00:09:56.288199+00:00","updated_at":"2026-05-18T00:09:56.288199+00:00"}