{"id":"https://openalex.org/W7131090884","doi":"https://doi.org/10.48550/arxiv.2602.18435","title":"CAKE: Confidence in Assignments via K-partition Ensembles","display_name":"CAKE: Confidence in Assignments via K-partition Ensembles","publication_year":2026,"publication_date":"2026-02-20","ids":{"openalex":"https://openalex.org/W7131090884","doi":"https://doi.org/10.48550/arxiv.2602.18435"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.18435","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119969847","display_name":"Aggelos Semoglou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Semoglou, Aggelos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124371572","display_name":"John Pavlopoulos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pavlopoulos, John","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20530165,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.1281999945640564,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.1281999945640564,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.0989999994635582,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.09139999747276306,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pointwise","display_name":"Pointwise","score":0.7152000069618225},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7064999938011169},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6779999732971191},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6008999943733215},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5906000137329102},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.4544999897480011},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4311000108718872},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.42010000348091125}],"concepts":[{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.7152000069618225},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7064999938011169},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6779999732971191},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6008999943733215},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5906000137329102},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5196999907493591},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.476500004529953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4546000063419342},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4341999888420105},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4311000108718872},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3862999975681305},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C2777615720","wikidata":"https://www.wikidata.org/wiki/Q11888847","display_name":"Prioritization","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.18435","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.18435","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.18435","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.18435","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Clustering":[0],"is":[1],"widely":[2],"used":[3,176],"for":[4,44,72,177],"unsupervised":[5],"structure":[6],"discovery,":[7],"yet":[8],"it":[9],"offers":[10],"limited":[11],"insight":[12],"into":[13,127],"how":[14],"reliable":[15],"each":[16,105],"individual":[17],"assignment":[18,116],"is.":[19],"Diagnostics,":[20],"such":[21],"as":[22],"convergence":[23],"behavior":[24],"or":[25,179],"objective":[26],"values,":[27],"may":[28],"reflect":[29],"global":[30,61],"quality,":[31],"but":[32,67],"they":[33,68],"do":[34],"not":[35],"indicate":[36,156],"whether":[37],"particular":[38],"instances":[39,172],"are":[40,125],"assigned":[41],"confidently,":[42],"especially":[43],"initialization-sensitive":[45],"algorithms":[46],"like":[47],"k-means.":[48],"This":[49,91],"assignment-level":[50],"instability":[51],"can":[52,174],"undermine":[53],"both":[54],"accuracy":[55],"and":[56,118,144,153,163],"robustness.":[57],"Ensemble":[58],"approaches":[59],"improve":[60],"consistency":[62,119],"by":[63],"aggregating":[64],"multiple":[65],"runs,":[66],"typically":[69],"lack":[70],"tools":[71],"quantifying":[73],"pointwise":[74],"confidence":[75,169],"in":[76,96,132,181],"a":[77,101,113,128,168],"way":[78],"that":[79,103,138,157,173],"combines":[80],"cross-run":[81],"agreement":[82],"with":[83],"geometric":[84,122],"support":[85],"from":[86,147],"the":[87],"learned":[88],"cluster":[89],"structure.":[90],"work":[92],"introduces":[93],"CAKE":[94,139,158],"(Confidence":[95],"Assignments":[97],"via":[98],"K-partition":[99],"Ensembles),":[100],"framework":[102],"evaluates":[104],"point":[106],"using":[107],"two":[108],"complementary":[109],"statistics":[110],"computed":[111],"over":[112,171],"clustering":[114,183],"ensemble:":[115],"stability":[117],"of":[120],"local":[121],"fit.":[123],"These":[124],"combined":[126],"single,":[129],"interpretable":[130],"score":[131],"[0,1].":[133],"The":[134],"theoretical":[135],"analysis":[136],"shows":[137],"remains":[140],"effective":[141],"under":[142],"noise":[143],"separates":[145],"stable":[146,164],"unstable":[148],"points.":[149],"Experiments":[150],"on":[151],"synthetic":[152],"real-world":[154],"datasets":[155],"effectively":[159],"highlights":[160],"ambiguous":[161],"points":[162],"core":[165],"members,":[166],"providing":[167],"ranking":[170],"be":[175],"selection":[178],"prioritization":[180],"downstream":[182],"workflows.":[184]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-24T00:00:00"}