{"id":"https://openalex.org/W2155176125","doi":"https://doi.org/10.1145/1376616.1376637","title":"CRD","display_name":"CRD","publication_year":2008,"publication_date":"2008-06-09","ids":{"openalex":"https://openalex.org/W2155176125","doi":"https://doi.org/10.1145/1376616.1376637","mag":"2155176125"},"language":"en","primary_location":{"id":"doi:10.1145/1376616.1376637","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1376616.1376637","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2008 ACM SIGMOD international conference on Management of data","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/A5033848098","display_name":"Feng Pan","orcid":"https://orcid.org/0000-0002-3403-0094"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Feng Pan","raw_affiliation_strings":["University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100368865","display_name":"Xiang Zhang","orcid":"https://orcid.org/0000-0002-5201-3802"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Zhang","raw_affiliation_strings":["University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392089","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8180-2886"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033848098"],"corresponding_institution_ids":["https://openalex.org/I114027177"],"apc_list":null,"apc_paid":null,"fwci":2.4583,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.90364906,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"173","last_page":"184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9980999827384949,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9962000250816345,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9934999942779541,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8946954011917114},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6793991327285767},{"id":"https://openalex.org/keywords/row","display_name":"Row","score":0.65946364402771},{"id":"https://openalex.org/keywords/biclustering","display_name":"Biclustering","score":0.6517350673675537},{"id":"https://openalex.org/keywords/row-and-column-spaces","display_name":"Row and column spaces","score":0.5927932262420654},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5712864398956299},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.5474739670753479},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5056376457214355},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.4388201832771301},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.43641796708106995},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.43280139565467834},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36980146169662476},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22876840829849243},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.06851738691329956}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8946954011917114},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6793991327285767},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.65946364402771},{"id":"https://openalex.org/C144817290","wikidata":"https://www.wikidata.org/wiki/Q2976575","display_name":"Biclustering","level":5,"score":0.6517350673675537},{"id":"https://openalex.org/C104140500","wikidata":"https://www.wikidata.org/wiki/Q2088159","display_name":"Row and column spaces","level":3,"score":0.5927932262420654},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5712864398956299},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.5474739670753479},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5056376457214355},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.4388201832771301},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.43641796708106995},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.43280139565467834},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36980146169662476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22876840829849243},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.06851738691329956},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1376616.1376637","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1376616.1376637","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2008 ACM SIGMOD international conference on Management of data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1493217831","https://openalex.org/W1572352401","https://openalex.org/W1902027874","https://openalex.org/W1989938746","https://openalex.org/W2003690406","https://openalex.org/W2013029404","https://openalex.org/W2036328877","https://openalex.org/W2043545458","https://openalex.org/W2060314721","https://openalex.org/W2111539174","https://openalex.org/W2134737843","https://openalex.org/W2135875435","https://openalex.org/W2136897707","https://openalex.org/W2138072998","https://openalex.org/W2141806397","https://openalex.org/W2147152072","https://openalex.org/W2158312129","https://openalex.org/W2434205482","https://openalex.org/W2571268788","https://openalex.org/W2798909945","https://openalex.org/W6750968397"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W2181939267","https://openalex.org/W2491448268","https://openalex.org/W3144143113","https://openalex.org/W2117838073","https://openalex.org/W3174322327","https://openalex.org/W4306940721","https://openalex.org/W2183916789","https://openalex.org/W2892323093","https://openalex.org/W2354012042"],"abstract_inverted_index":{"The":[0,48],"problem":[1],"of":[2,50,66,86],"simultaneously":[3],"clustering":[4,28,42],"columns":[5,69,87],"and":[6,20,61,68,88,143,166],"rows":[7,67],"(co-clustering)":[8],"arises":[9],"in":[10,39,44,70,99,141,155],"important":[11],"applications,":[12],"such":[13],"as":[14],"text":[15],"data":[16,46,72,80,152],"mining,":[17],"microarray":[18],"analysis,":[19],"recommendation":[21],"system":[22],"analysis.":[23],"Compared":[24,169],"with":[25,170,179],"the":[26,45,64,71,106,110,150,156],"classical":[27],"algorithms,":[29,173],"co-clustering":[30,52,103,123,172],"algorithms":[31,53],"have":[32],"been":[33],"shown":[34],"to":[35,79],"be":[36,96,154],"more":[37],"effective":[38],"discovering":[40],"hidden":[41],"structures":[43],"matrix.":[47],"complexity":[49],"previous":[51,111,171],"is":[54],"usually":[55],"O(m":[56],"X":[57],"n),":[58],"where":[59],"m":[60,142],"n":[62],"are":[63],"numbers":[65],"matrix":[73,132,153],"respectively.":[74],"This":[75],"limits":[76],"their":[77],"applicability":[78],"matrices":[81],"involving":[82],"a":[83,118],"large":[84,124],"number":[85],"rows.":[89],"Moreover,":[90],"some":[91],"huge":[92],"datasets":[93],"can":[94],"not":[95,148],"entirely":[97],"held":[98],"main":[100,157],"memory":[101],"during":[102],"which":[104],"violates":[105],"assumption":[107],"made":[108],"by":[109],"algorithms.":[112],"In":[113],"this":[114],"paper,":[115],"we":[116],"propose":[117],"general":[119],"framework":[120],"for":[121],"fast":[122],"datasets,":[125],"CRD.":[126],"By":[127],"utilizing":[128],"recently":[129],"developed":[130],"sampling-based":[131],"decomposition":[133],"methods,":[134],"CRD":[135,146,174],"achieves":[136,175],"an":[137],"execution":[138],"time":[139],"linear":[140],"n.":[144],"Also,":[145],"does":[147],"require":[149],"whole":[151],"memory.":[158],"We":[159],"conducted":[160],"extensive":[161],"experiments":[162],"on":[163],"both":[164],"real":[165],"synthetic":[167],"data.":[168],"competitive":[176],"accuracy":[177],"but":[178],"much":[180],"less":[181],"computational":[182],"cost.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2016-06-24T00:00:00"}