{"id":"https://openalex.org/W4414359865","doi":"https://doi.org/10.24963/ijcai.2025/756","title":"Multi-view Clustering via Multi-granularity Ensemble","display_name":"Multi-view Clustering via Multi-granularity Ensemble","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414359865","doi":"https://doi.org/10.24963/ijcai.2025/756"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/756","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100404947","display_name":"Jie Yang","orcid":"https://orcid.org/0000-0003-4801-7162"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["University of Technology Sydney"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100344582","display_name":"Wei Chen","orcid":"https://orcid.org/0000-0003-3720-718X"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wei Chen","raw_affiliation_strings":["The University of Sydney"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Sydney","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100639398","display_name":"Feng Liu","orcid":"https://orcid.org/0000-0002-5005-9129"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Feng Liu","raw_affiliation_strings":["The University of Melbourne"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101424705","display_name":"Peng Zhou","orcid":"https://orcid.org/0000-0002-3607-0022"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhou","raw_affiliation_strings":["Anhui University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anhui University","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100674337","display_name":"Zhongli Wang","orcid":"https://orcid.org/0009-0009-6158-9457"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongli Wang","raw_affiliation_strings":["Hangzhou Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Normal University","institution_ids":["https://openalex.org/I163151501"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076391595","display_name":"Xinyan Liang","orcid":"https://orcid.org/0000-0003-2589-5392"},"institutions":[{"id":"https://openalex.org/I4210142037","display_name":"Shanxi University of Traditional Chinese Medicine","ror":"https://ror.org/0522dg826","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142037"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyan Liang","raw_affiliation_strings":["Shanxi University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanxi University","institution_ids":["https://openalex.org/I4210142037"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034037411","display_name":"Bingbing Jiang","orcid":"https://orcid.org/0009-0006-3153-6125"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingbing Jiang","raw_affiliation_strings":["Hangzhou Normal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Normal University","institution_ids":["https://openalex.org/I163151501"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6794","last_page":"6802"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9703999757766724,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9703999757766724,"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/T10057","display_name":"Face and Expression Recognition","score":0.9100000262260437,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/cluster-analysis","display_name":"Cluster analysis","score":0.7975000143051147},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5914999842643738},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5360999703407288},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4699999988079071},{"id":"https://openalex.org/keywords/consensus-clustering","display_name":"Consensus clustering","score":0.44769999384880066},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.387800008058548},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.37380000948905945},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.3596000075340271},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.34299999475479126}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7975000143051147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7319999933242798},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5914999842643738},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5443000197410583},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5360999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5073999762535095},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.44769999384880066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.415800005197525},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.387800008058548},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.34299999475479126},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.28189998865127563},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C144817290","wikidata":"https://www.wikidata.org/wiki/Q2976575","display_name":"Biclustering","level":5,"score":0.2621999979019165},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2988416141","wikidata":"https://www.wikidata.org/wiki/Q6031139","display_name":"Information loss","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.25540000200271606},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/756","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/756","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"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":{"Multi-view":[0],"clustering":[1,12,73,98],"aims":[2],"to":[3,10,23,110,123],"integrate":[4],"complementary":[5],"information":[6,20,83],"from":[7,19],"multiple":[8,177],"views":[9,59,86],"improve":[11],"performance.":[13],"However,":[14],"existing":[15],"ensemble-based":[16],"methods":[17,175],"suffer":[18],"loss":[21],"due":[22],"their":[24],"reliance":[25],"on":[26,103],"single-granularity":[27],"labels,":[28],"limiting":[29],"the":[30,81,96,107,115,133,160],"discriminative":[31],"capability":[32],"of":[33,138,152],"learned":[34],"representations.":[35],"Meanwhile,":[36],"representation":[37,151],"and":[38,49,89,99,117,135,141,154,165,182],"graph":[39],"fusion-based":[40],"approaches":[41],"face":[42],"challenges":[43],"such":[44],"as":[45],"explicit":[46],"view":[47,105,163],"alignment":[48,164],"manual":[50],"weight":[51,166],"tuning,":[52],"making":[53],"them":[54],"less":[55],"effective":[56],"for":[57,87,162],"heterogeneous":[58,186],"with":[60],"varying":[61],"data":[62,140],"distributions.":[63],"To":[64],"address":[65],"these":[66],"limitations,":[67],"we":[68],"propose":[69],"a":[70,125,149],"novel":[71],"multi-view":[72,139],"framework":[74],"via":[75],"Multi-granularity":[76],"Ensemble":[77],"(MGE),":[78],"fully":[79],"using":[80],"multi-granularity":[82,112],"across":[84,176],"diverse":[85],"accurate":[88],"consistent":[90],"clustering.":[91,144],"Specifically,":[92],"MGE":[93,146,171],"first":[94],"modifies":[95],"hierarchical":[97],"then":[100],"leverages":[101],"it":[102],"each":[104],"(including":[106],"fused":[108],"view)":[109],"achieve":[111],"labels.":[113],"Moreover,":[114],"cross-view":[116],"cross-granularity":[118],"fusion":[119],"strategy":[120],"is":[121],"designed":[122],"learn":[124],"robust":[126],"co-association":[127],"similarity":[128],"matrix,":[129],"which":[130],"effectively":[131],"preserves":[132],"fine-grained":[134],"coarse-grained":[136],"structures":[137],"facilitates":[142],"subsequent":[143],"Therefore,":[145],"can":[147],"provide":[148],"comprehensive":[150],"local":[153],"global":[155],"patterns":[156],"within":[157],"data,":[158],"eliminating":[159],"requirement":[161],"tuning.":[167],"Experiments":[168],"demonstrate":[169],"that":[170],"consistently":[172],"outperforms":[173],"state-of-the-art":[174],"datasets,":[178],"validating":[179],"its":[180],"effectiveness":[181],"superiority":[183],"in":[184],"handling":[185],"views.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}