{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JBYTRVPYCGTGUWHLMSYNFCO5IE","short_pith_number":"pith:JBYTRVPY","schema_version":"1.0","canonical_sha256":"487138d5f811a66a58eb64b0d289dd410f36adc55de7dec4c21c0e5049c3ec4a","source":{"kind":"arxiv","id":"1810.03419","version":1},"attestation_state":"computed","paper":{"title":"Unique Metric for Health Analysis with Optimization of Clustering Activity and Cross Comparison of Results from Different Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jitin Kapila, Kumarjit Pathak","submitted_at":"2018-10-08T13:10:54Z","abstract_excerpt":"In machine learning and data mining, Cluster analysis is one of the most widely used unsupervised learning technique. Philosophy of this algorithm is to find similar data items and group them together based on any distance function in multidimensional space. These methods are suitable for finding groups of data that behave in a coherent fashion. The perspective may vary for clustering i.e. the way we want to find similarity, some methods are based on distance such as K-Means technique and some are probability based, like GMM. Understanding prominent segment of data is always challenging as mul"},"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":"1810.03419","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2018-10-08T13:10:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8cbfec910b035ee0aa79fb9774fb0802d5a09a17935f165ec917c7581674a249","abstract_canon_sha256":"2380c4a7dcb647c239f7351500097323c45d682042cb45b76051b3ced20e02de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:51.347109Z","signature_b64":"lxXZSO7iObFQBMGhawfbMQH6mu/atZEZKpzP9DDS1f3tuABtHumuQsfYWYuM39NxE8s/9sBoESrl8hX5n3nrDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"487138d5f811a66a58eb64b0d289dd410f36adc55de7dec4c21c0e5049c3ec4a","last_reissued_at":"2026-05-18T00:03:51.346499Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:51.346499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unique Metric for Health Analysis with Optimization of Clustering Activity and Cross Comparison of Results from Different Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jitin Kapila, Kumarjit Pathak","submitted_at":"2018-10-08T13:10:54Z","abstract_excerpt":"In machine learning and data mining, Cluster analysis is one of the most widely used unsupervised learning technique. Philosophy of this algorithm is to find similar data items and group them together based on any distance function in multidimensional space. These methods are suitable for finding groups of data that behave in a coherent fashion. The perspective may vary for clustering i.e. the way we want to find similarity, some methods are based on distance such as K-Means technique and some are probability based, like GMM. Understanding prominent segment of data is always challenging as mul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.03419","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":"1810.03419","created_at":"2026-05-18T00:03:51.346579+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.03419v1","created_at":"2026-05-18T00:03:51.346579+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.03419","created_at":"2026-05-18T00:03:51.346579+00:00"},{"alias_kind":"pith_short_12","alias_value":"JBYTRVPYCGTG","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"JBYTRVPYCGTGUWHL","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"JBYTRVPY","created_at":"2026-05-18T12:32:31.084164+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/JBYTRVPYCGTGUWHLMSYNFCO5IE","json":"https://pith.science/pith/JBYTRVPYCGTGUWHLMSYNFCO5IE.json","graph_json":"https://pith.science/api/pith-number/JBYTRVPYCGTGUWHLMSYNFCO5IE/graph.json","events_json":"https://pith.science/api/pith-number/JBYTRVPYCGTGUWHLMSYNFCO5IE/events.json","paper":"https://pith.science/paper/JBYTRVPY"},"agent_actions":{"view_html":"https://pith.science/pith/JBYTRVPYCGTGUWHLMSYNFCO5IE","download_json":"https://pith.science/pith/JBYTRVPYCGTGUWHLMSYNFCO5IE.json","view_paper":"https://pith.science/paper/JBYTRVPY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.03419&json=true","fetch_graph":"https://pith.science/api/pith-number/JBYTRVPYCGTGUWHLMSYNFCO5IE/graph.json","fetch_events":"https://pith.science/api/pith-number/JBYTRVPYCGTGUWHLMSYNFCO5IE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JBYTRVPYCGTGUWHLMSYNFCO5IE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JBYTRVPYCGTGUWHLMSYNFCO5IE/action/storage_attestation","attest_author":"https://pith.science/pith/JBYTRVPYCGTGUWHLMSYNFCO5IE/action/author_attestation","sign_citation":"https://pith.science/pith/JBYTRVPYCGTGUWHLMSYNFCO5IE/action/citation_signature","submit_replication":"https://pith.science/pith/JBYTRVPYCGTGUWHLMSYNFCO5IE/action/replication_record"}},"created_at":"2026-05-18T00:03:51.346579+00:00","updated_at":"2026-05-18T00:03:51.346579+00:00"}