{"id":"W1987209614","doi":"10.5539/cis.v1n3p66","title":"Similarity Matrix Based Session Clustering by Sequence Alignment Using Dynamic Programming","year":2008,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Session (web analytics); Cluster analysis; Web mining; Hierarchical clustering; Similarity (geometry); Information retrieval; World Wide Web; Data mining; Web service; Database transaction; Database; Machine learning; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001388109,0.0009201163,0.001528862,0.00323048,0.001201118,0.001510019,0.001776921,0.0009857321,0.002085371],"category_scores_gemma":[0.004038586,0.0006089279,0.001295406,0.003959943,0.0007498293,0.002009922,0.001226907,0.001218655,0.0009120261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001305931,"about_ca_system_score_gemma":0.002111481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007641132,"about_ca_topic_score_gemma":0.006257354,"domain_scores_codex":[0.9978958,0.0005217611,0.000143484,0.0007191008,0.0005411732,0.0001786437],"domain_scores_gemma":[0.9983369,0.0007235111,0.0002423693,0.0001454358,0.0004588558,0.00009279051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003568069,0.0003879117,0.003226657,0.0002109932,0.0001738756,0.0001709246,0.0004316345,0.5447003,0.01213742,0.02187206,0.003469832,0.4128615],"study_design_scores_gemma":[0.000009982132,0.00005004392,0.0003204814,0.000005965051,0.00001507113,0.00005930142,0.00005352953,0.9886546,0.002290799,0.007329861,0.001195926,0.00001439899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00808376,0.00005218252,0.9905385,0.00005510106,0.00001455849,0.0001015706,0.0000806751,0.0006230614,0.0004507067],"genre_scores_gemma":[0.1548903,0.0001399677,0.8407398,0.0000564944,0.00003572486,0.0005979724,0.0009220915,0.0002109493,0.002406853],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007641132,"threshold_uncertainty_score":0.01519334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168089812779665,"score_gpt":0.3087568984440514,"score_spread":0.2770760003162547,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}