{"id":"W2155874464","doi":"10.1109/icpr.2008.4761242","title":"A novel validity measure for clusters of arbitrary shapes and densities","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Measure (data warehouse); Cluster analysis; Computer science; Cluster (spacecraft); Neighbourhood (mathematics); Mathematics; Algorithm; Data mining; Statistical physics; Artificial intelligence; Physics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008467521,0.0009316335,0.0008065572,0.00135819,0.000506995,0.0009644321,0.002057659,0.0004056109,0.0004929767],"category_scores_gemma":[0.0005841907,0.0009958699,0.0003459486,0.0004261313,0.0004638541,0.002889367,0.0006088825,0.000971769,0.0001388537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000411059,"about_ca_system_score_gemma":0.0002376478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009642624,"about_ca_topic_score_gemma":0.00002825973,"domain_scores_codex":[0.9933368,0.00003136754,0.001341141,0.001984761,0.002402392,0.0009035192],"domain_scores_gemma":[0.9903564,0.0002983761,0.001178782,0.0002096125,0.007537101,0.0004196677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003081996,0.003687521,0.01665089,0.001588816,0.001989366,0.00008859793,0.007962864,0.00002454016,0.06600304,0.03924328,0.002745419,0.8569337],"study_design_scores_gemma":[0.0179422,0.005981394,0.02648896,0.009404159,0.000301924,0.002588447,0.006574573,0.7282847,0.1099222,0.0853374,0.0009161702,0.006257938],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6241953,0.00002418596,0.3220786,0.007917323,0.002108865,0.002214087,0.002241916,0.0006818919,0.03853791],"genre_scores_gemma":[0.9824761,0.0004960397,0.01331443,0.001371385,0.0007244535,0.000728935,0.0004501046,0.0001041171,0.0003344275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8506757,"threshold_uncertainty_score":0.9992492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1958721890923441,"score_gpt":0.3282209827293366,"score_spread":0.1323487936369925,"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."}}