{"id":"https://openalex.org/W2902808875","doi":"https://doi.org/10.1109/icpr.2018.8545488","title":"Graph Embedding-Based Ensemble Learning for Image Clustering","display_name":"Graph Embedding-Based Ensemble Learning for Image Clustering","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2902808875","doi":"https://doi.org/10.1109/icpr.2018.8545488","mag":"2902808875"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545488","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"article","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/A5003572308","display_name":"Xiaohui Luo","orcid":"https://orcid.org/0000-0002-2978-7180"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaohui Luo","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425448","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0001-7914-0679"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013713640","display_name":"Fanzhang Li","orcid":"https://orcid.org/0000-0003-4318-3081"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanzhang Li","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075719824","display_name":"Bangjun Wang","orcid":"https://orcid.org/0000-0003-1372-2486"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bangjun Wang","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5003572308"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.3016,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57829859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"290","issue":null,"first_page":"213","last_page":"218"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9976000189781189,"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":0.9975000023841858,"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":0.8482270240783691},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5875479578971863},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5858651399612427},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.5704299211502075},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5347489714622498},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5317902565002441},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5260460376739502},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5219844579696655},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.5196318030357361},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5155539512634277},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48904284834861755},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.4749617278575897},{"id":"https://openalex.org/keywords/consensus-clustering","display_name":"Consensus clustering","score":0.4233390688896179},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4164179265499115},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.4105289578437805},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.341408371925354},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15186619758605957}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8482270240783691},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5875479578971863},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5858651399612427},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.5704299211502075},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5347489714622498},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5317902565002441},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5260460376739502},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5219844579696655},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.5196318030357361},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5155539512634277},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48904284834861755},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.4749617278575897},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.4233390688896179},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4164179265499115},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.4105289578437805},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.341408371925354},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15186619758605957}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545488","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1498369939","https://openalex.org/W1500351990","https://openalex.org/W1510047790","https://openalex.org/W1578099820","https://openalex.org/W1578196132","https://openalex.org/W1926592634","https://openalex.org/W2001141328","https://openalex.org/W2004026774","https://openalex.org/W2010835048","https://openalex.org/W2025268122","https://openalex.org/W2025942659","https://openalex.org/W2053186076","https://openalex.org/W2075880488","https://openalex.org/W2099454382","https://openalex.org/W2103250033","https://openalex.org/W2129838208","https://openalex.org/W2133288847","https://openalex.org/W2139172286","https://openalex.org/W2148694408","https://openalex.org/W2154872931","https://openalex.org/W2156718197","https://openalex.org/W2288381231","https://openalex.org/W2415145453","https://openalex.org/W2577472518","https://openalex.org/W2604983939","https://openalex.org/W2754064832","https://openalex.org/W3033414884","https://openalex.org/W3122665668","https://openalex.org/W4213263325","https://openalex.org/W4236619680","https://openalex.org/W6629858893","https://openalex.org/W6675955514","https://openalex.org/W6682644385","https://openalex.org/W6682755970","https://openalex.org/W6731795620","https://openalex.org/W6736218809","https://openalex.org/W6776535907"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W2181939267","https://openalex.org/W2491448268","https://openalex.org/W4306940721","https://openalex.org/W3144143113","https://openalex.org/W2117838073","https://openalex.org/W3174322327","https://openalex.org/W2183916789","https://openalex.org/W2354012042","https://openalex.org/W3124860551"],"abstract_inverted_index":{"As":[0],"a":[1,26,95,118],"manifold":[2],"learning":[3,30,101],"algorithm,":[4],"unsupervised":[5],"large":[6,151],"graph":[7,27],"embedding":[8,71],"(ULGE)":[9],"has":[10],"been":[11],"proposed":[12,162],"to":[13,65,141,145],"deal":[14],"with":[15,117],"large-scale":[16],"dataset":[17],"for":[18],"clustering.":[19,96,149],"This":[20],"paper":[21],"improves":[22],"ULGE":[23,40],"and":[24,41,86,158],"proposes":[25],"embedding-based":[28],"ensemble":[29,52,100],"(GEEL)":[31],"algorithm.":[32],"We":[33],"take":[34],"the":[35,42,58,69,73,79,89,104,122,126,147,156,161],"dimensionality":[36],"reduction":[37],"algorithm":[38,45],"in":[39,50,94],"K-means":[43,59,80],"clustering":[44,60,81,120,128],"as":[46],"an":[47],"individual":[48,56],"learner":[49],"our":[51],"learning.":[53],"For":[54],"each":[55],"learner,":[57],"method":[61,82],"is":[62,76,83,129,139],"first":[63,113],"used":[64,84],"generate":[66,146],"anchors.":[67],"Then,":[68],"low-dimensional":[70,90],"of":[72,99,106,153,160],"sample":[74],"data":[75],"obtained.":[77],"Finally,":[78],"again":[85],"performed":[87],"on":[88,103],"data,":[91],"which":[92],"results":[93],"The":[97],"diversity":[98],"lies":[102],"unstable":[105],"K-means.":[107],"To":[108],"combine":[109],"multiple":[110,133],"clusterings,":[111],"we":[112],"match":[114],"these":[115,142],"clusterings":[116,144],"reference":[119,127],"using":[121],"bestMap":[123],"method,":[124],"where":[125],"randomly":[130],"chosen":[131],"from":[132],"ones.":[134],"A":[135,150],"majority":[136],"voting":[137],"rule":[138],"adopted":[140],"matched":[143],"final":[148],"number":[152],"experiments":[154],"show":[155],"efficiency":[157],"effectiveness":[159],"method.":[163]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
