{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IJORGWUARRT47HMRXJ2JSPL3X3","short_pith_number":"pith:IJORGWUA","schema_version":"1.0","canonical_sha256":"425d135a808c67cf9d91ba74993d7bbec14ec27dc4a614396307a84091f1db1b","source":{"kind":"arxiv","id":"2511.17823","version":2},"attestation_state":"computed","paper":{"title":"A novel k-means clustering approach using two distance measures for Gaussian data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Naitik Gada (1) ((1) Rochester Institute of Technology)","submitted_at":"2025-11-21T22:36:14Z","abstract_excerpt":"Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning. Since clustering analysis is one of the best ways to find some clarity and structure within raw data, this paper explores a novel approach to k-means clustering. Here we present a k-means clustering algorithm that takes both the within cluster distance (WCD) and the inter cluster distance (ICD) as the distance metric to cluster the data into k clusters pre-determined by the Calinski-Harabasz criterion in order to provide a more robust output for the clustering analysis. The "},"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":"2511.17823","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-11-21T22:36:14Z","cross_cats_sorted":["cs.CE","stat.ME","stat.ML"],"title_canon_sha256":"7853aa0225ac25a9a8d3f33a20bbbb9ea31e0d63153d74068c137cbc0759fcbd","abstract_canon_sha256":"ebaf6a3f7400deaf0febafcb16280de4d4d4f8a13eee2193bc876de9aaf29b86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"425d135a808c67cf9d91ba74993d7bbec14ec27dc4a614396307a84091f1db1b","last_reissued_at":"2026-07-31T00:10:11.018588Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T00:10:11.018588Z"},"graph_snapshot":{"paper":{"title":"A novel k-means clustering approach using two distance measures for Gaussian data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Naitik Gada (1) ((1) Rochester Institute of Technology)","submitted_at":"2025-11-21T22:36:14Z","abstract_excerpt":"Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning. Since clustering analysis is one of the best ways to find some clarity and structure within raw data, this paper explores a novel approach to k-means clustering. Here we present a k-means clustering algorithm that takes both the within cluster distance (WCD) and the inter cluster distance (ICD) as the distance metric to cluster the data into k clusters pre-determined by the Calinski-Harabasz criterion in order to provide a more robust output for the clustering analysis. The "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.17823","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.17823/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2511.17823","created_at":"2026-07-31T00:10:11.021077+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.17823v2","created_at":"2026-07-31T00:10:11.021077+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.17823","created_at":"2026-07-31T00:10:11.021077+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJORGWUARRT4","created_at":"2026-07-31T00:10:11.021077+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJORGWUARRT47HMR","created_at":"2026-07-31T00:10:11.021077+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJORGWUA","created_at":"2026-07-31T00:10:11.021077+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/IJORGWUARRT47HMRXJ2JSPL3X3","json":"https://pith.science/pith/IJORGWUARRT47HMRXJ2JSPL3X3.json","graph_json":"https://pith.science/api/pith-number/IJORGWUARRT47HMRXJ2JSPL3X3/graph.json","events_json":"https://pith.science/api/pith-number/IJORGWUARRT47HMRXJ2JSPL3X3/events.json","paper":"https://pith.science/paper/IJORGWUA"},"agent_actions":{"view_html":"https://pith.science/pith/IJORGWUARRT47HMRXJ2JSPL3X3","download_json":"https://pith.science/pith/IJORGWUARRT47HMRXJ2JSPL3X3.json","view_paper":"https://pith.science/paper/IJORGWUA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.17823&json=true","fetch_graph":"https://pith.science/api/pith-number/IJORGWUARRT47HMRXJ2JSPL3X3/graph.json","fetch_events":"https://pith.science/api/pith-number/IJORGWUARRT47HMRXJ2JSPL3X3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJORGWUARRT47HMRXJ2JSPL3X3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJORGWUARRT47HMRXJ2JSPL3X3/action/storage_attestation","attest_author":"https://pith.science/pith/IJORGWUARRT47HMRXJ2JSPL3X3/action/author_attestation","sign_citation":"https://pith.science/pith/IJORGWUARRT47HMRXJ2JSPL3X3/action/citation_signature","submit_replication":"https://pith.science/pith/IJORGWUARRT47HMRXJ2JSPL3X3/action/replication_record"}},"created_at":"2026-07-31T00:10:11.021077+00:00","updated_at":"2026-07-31T00:10:11.021077+00:00"}