{"id":"https://openalex.org/W2913688486","doi":"https://doi.org/10.1109/bigdata.2018.8621917","title":"Fast Clustering with Flexible Balance Constraints","display_name":"Fast Clustering with Flexible Balance Constraints","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2913688486","doi":"https://doi.org/10.1109/bigdata.2018.8621917","mag":"2913688486"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8621917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8621917","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","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/A5086089915","display_name":"Hongfu Liu","orcid":"https://orcid.org/0000-0002-4261-8154"},"institutions":[{"id":"https://openalex.org/I6902469","display_name":"Brandeis University","ror":"https://ror.org/05abbep66","country_code":"US","type":"education","lineage":["https://openalex.org/I6902469"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongfu Liu","raw_affiliation_strings":["Department of Computer Science, Brandeis University, Waltham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Brandeis University, Waltham","institution_ids":["https://openalex.org/I6902469"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101880338","display_name":"Ziming Huang","orcid":"https://orcid.org/0009-0009-9890-1395"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziming Huang","raw_affiliation_strings":["Key Lab of High Confidence Software Technologies (MOE), School of EECS, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab of High Confidence Software Technologies (MOE), School of EECS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100340143","display_name":"Qi Chen","orcid":"https://orcid.org/0000-0001-9367-4757"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi Chen","raw_affiliation_strings":["Microsoft, Northeastern University, Boston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Northeastern University, Boston","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103208235","display_name":"Mingqin Li","orcid":"https://orcid.org/0009-0002-0270-9489"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mingqin Li","raw_affiliation_strings":["Microsoft, Northeastern University, Boston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Northeastern University, Boston","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005819096","display_name":"Yun Fu","orcid":"https://orcid.org/0000-0002-5098-2853"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yun Fu","raw_affiliation_strings":["College of Engineering, Northeastern University, Boston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering, Northeastern University, Boston","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101985940","display_name":"Lintao Zhang","orcid":"https://orcid.org/0009-0005-7527-8183"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lintao Zhang","raw_affiliation_strings":["Microsoft, Northeastern University, Boston"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, Northeastern University, Boston","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1132,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.45323633,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"743","last_page":"750"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9965999722480774,"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"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9958000183105469,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8847253918647766},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7788106799125671},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.6414633393287659},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5525898933410645},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5267797708511353},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.5001430511474609},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.49466702342033386},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.48380225896835327},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4775743782520294},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.4596765339374542},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.260503351688385}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8847253918647766},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7788106799125671},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.6414633393287659},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5525898933410645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5267797708511353},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.5001430511474609},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.49466702342033386},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.48380225896835327},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4775743782520294},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.4596765339374542},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.260503351688385},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2018.8621917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8621917","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.550000011920929,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W2283060","https://openalex.org/W79405465","https://openalex.org/W97540112","https://openalex.org/W154649636","https://openalex.org/W1565377632","https://openalex.org/W1606487094","https://openalex.org/W1673310716","https://openalex.org/W1968876140","https://openalex.org/W1993962865","https://openalex.org/W1997201895","https://openalex.org/W1998325344","https://openalex.org/W2003217181","https://openalex.org/W2011430131","https://openalex.org/W2011494630","https://openalex.org/W2055996587","https://openalex.org/W2073459066","https://openalex.org/W2097253747","https://openalex.org/W2107221597","https://openalex.org/W2113540758","https://openalex.org/W2127218421","https://openalex.org/W2129630294","https://openalex.org/W2136189984","https://openalex.org/W2149521737","https://openalex.org/W2162165238","https://openalex.org/W2386997096","https://openalex.org/W2565425623","https://openalex.org/W2604578492","https://openalex.org/W2742098698","https://openalex.org/W2750102740","https://openalex.org/W2801999749","https://openalex.org/W4205774955","https://openalex.org/W6603183647","https://openalex.org/W6637131181","https://openalex.org/W6651074279","https://openalex.org/W6668990524","https://openalex.org/W6674500583","https://openalex.org/W6678914141","https://openalex.org/W6680450716","https://openalex.org/W6683808202","https://openalex.org/W6731212744","https://openalex.org/W6735907395"],"related_works":["https://openalex.org/W2160785859","https://openalex.org/W2390610678","https://openalex.org/W3140018618","https://openalex.org/W3146523624","https://openalex.org/W2188840951","https://openalex.org/W2241871771","https://openalex.org/W2171583777","https://openalex.org/W2555816786","https://openalex.org/W2407786351","https://openalex.org/W2773356553"],"abstract_inverted_index":{"Balanced":[0],"clustering":[1,35,72,89,110,175],"aims":[2],"at":[3],"partitioning":[4],"a":[5,59,67,120,178],"dataset":[6],"with":[7,54,62,77,153,193],"roughly":[8],"even":[9],"cluster":[10,93],"sizes":[11],"while":[12,212],"exploiting":[13,119],"the":[14,18,27,30,87,92,97,106,115,124,202,208,219],"intrinsic":[15],"structure":[16],"of":[17,105,123,172],"data.":[19],"Despite":[20],"attracting":[21],"increased":[22],"attention":[23],"recently":[24],"in":[25,170,190],"both":[26],"academia":[28],"and":[29,70,91,174,196],"industry,":[31],"most":[32],"existing":[33],"balanced":[34],"algorithms":[36],"still":[37],"have":[38],"high":[39],"run":[40],"time":[41],"complexities":[42],"that":[43,74,137,160],"prevent":[44],"them":[45],"from":[46],"being":[47],"applied":[48],"to":[49,130,140,218],"large":[50,168],"datasets.":[51,142],"To":[52],"cope":[53],"this":[55],"challenge,":[56],"we":[57,144],"propose":[58],"Fast":[60],"Clustering":[61],"Flexible":[63],"balance":[64,79,151],"Constraints":[65],"FCFC,":[66],"simple,":[68],"fast":[69],"effective":[71],"algorithm":[73,90],"can":[75,138],"deal":[76],"flexible":[78],"constraints.":[80],"In":[81,201],"essence,":[82],"FCFC":[83,127],"employs":[84],"K-means":[85,109],"as":[86,96,112,114],"core":[88],"size":[94,195],"variances":[95],"penalty":[98],"for":[99,149,181],"imbalance.":[100],"The":[101],"objective":[102],"function":[103],"consists":[104],"combined":[107],"classical":[108],"cost":[111,214],"well":[113],"imbalance":[116],"penalty.":[117],"By":[118],"new":[121],"insight":[122],"second":[125],"term,":[126],"is":[128,184],"able":[129],"employ":[131],"an":[132],"efficient":[133],"K-means-like":[134],"optimization":[135],"procedure":[136],"scale":[139],"big":[141],"Furthermore,":[143],"also":[145],"extend":[146],"our":[147,161,205],"model":[148],"multiple":[150,191],"constraints":[152],"theoretical":[154],"supports.":[155],"Extensive":[156],"experimental":[157],"results":[158],"show":[159],"method":[162],"exceeds":[163],"several":[164],"state-of-the-art":[165],"methods":[166],"by":[167,215],"margins":[169],"terms":[171],"efficiency":[173],"quality.":[176],"Finally,":[177],"real-world":[179],"application":[180],"Bing":[182],"search":[183],"provided,":[185],"where":[186],"data":[187,194],"are":[188],"organized":[189],"machines":[192],"query":[197],"frequency":[198],"balancing":[199],"objectives.":[200],"simulated":[203],"scenario,":[204],"solution":[206],"achieves":[207],"same":[209],"fidelity":[210],"score":[211],"reduces":[213],"75%":[216],"compared":[217],"baseline":[220],"method.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
