{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:CGEIWCRJ6XDMSFUIZS3BRIZ5ME","short_pith_number":"pith:CGEIWCRJ","schema_version":"1.0","canonical_sha256":"11888b0a29f5c6c91688ccb618a33d611424287d626cdadf43308b686581fba9","source":{"kind":"arxiv","id":"1507.03340","version":1},"attestation_state":"computed","paper":{"title":"Quantitative Evaluation of Performance and Validity Indices for Clustering the Web Navigational Sessions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.LG","authors_text":"A.Vinaya Babu, M.F. Azeem, Waseem Ahmed, Zahid Ansari","submitted_at":"2015-07-13T07:15:06Z","abstract_excerpt":"Clustering techniques are widely used in Web Usage Mining to capture similar interests and trends among users accessing a Web site. For this purpose, web access logs generated at a particular web site are preprocessed to discover the user navigational sessions. Clustering techniques are then applied to group the user session data into user session clusters, where intercluster similarities are minimized while the intra cluster similarities are maximized. Since the application of different clustering algorithms generally results in different sets of cluster formation, it is important to evaluate"},"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":"1507.03340","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-13T07:15:06Z","cross_cats_sorted":["cs.SI"],"title_canon_sha256":"bfdde19a83183b2add0845a5331b0f2aa57b0e77af14aecc6fac221fa8412efe","abstract_canon_sha256":"4424efde039e44a7fe4df299d8504b26125eb129701eccdf2b0952dd97fc43e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:59.045502Z","signature_b64":"Hnq0y8Hj+mQcFO7fn0hIGmlWkJvbeMd4qqRka0qISI2VrIKh5X5tBkfoRckWYyHXZFLI91Sx72TyF2TYlgXmDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11888b0a29f5c6c91688ccb618a33d611424287d626cdadf43308b686581fba9","last_reissued_at":"2026-05-18T01:36:59.044878Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:59.044878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantitative Evaluation of Performance and Validity Indices for Clustering the Web Navigational Sessions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI"],"primary_cat":"cs.LG","authors_text":"A.Vinaya Babu, M.F. Azeem, Waseem Ahmed, Zahid Ansari","submitted_at":"2015-07-13T07:15:06Z","abstract_excerpt":"Clustering techniques are widely used in Web Usage Mining to capture similar interests and trends among users accessing a Web site. For this purpose, web access logs generated at a particular web site are preprocessed to discover the user navigational sessions. Clustering techniques are then applied to group the user session data into user session clusters, where intercluster similarities are minimized while the intra cluster similarities are maximized. Since the application of different clustering algorithms generally results in different sets of cluster formation, it is important to evaluate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.03340","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":"1507.03340","created_at":"2026-05-18T01:36:59.044962+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.03340v1","created_at":"2026-05-18T01:36:59.044962+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.03340","created_at":"2026-05-18T01:36:59.044962+00:00"},{"alias_kind":"pith_short_12","alias_value":"CGEIWCRJ6XDM","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_16","alias_value":"CGEIWCRJ6XDMSFUI","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_8","alias_value":"CGEIWCRJ","created_at":"2026-05-18T12:29:14.074870+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.14649","citing_title":"Cleanse: Uncertainty Estimation Approach Using Clustering-based Semantic Consistency in LLMs","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME","json":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME.json","graph_json":"https://pith.science/api/pith-number/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/graph.json","events_json":"https://pith.science/api/pith-number/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/events.json","paper":"https://pith.science/paper/CGEIWCRJ"},"agent_actions":{"view_html":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME","download_json":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME.json","view_paper":"https://pith.science/paper/CGEIWCRJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.03340&json=true","fetch_graph":"https://pith.science/api/pith-number/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/graph.json","fetch_events":"https://pith.science/api/pith-number/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/action/storage_attestation","attest_author":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/action/author_attestation","sign_citation":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/action/citation_signature","submit_replication":"https://pith.science/pith/CGEIWCRJ6XDMSFUIZS3BRIZ5ME/action/replication_record"}},"created_at":"2026-05-18T01:36:59.044962+00:00","updated_at":"2026-05-18T01:36:59.044962+00:00"}