{"id":"https://openalex.org/W1998819761","doi":"https://doi.org/10.1145/1631162.1631165","title":"On evolutionary spectral clustering","display_name":"On evolutionary spectral clustering","publication_year":2009,"publication_date":"2009-11-01","ids":{"openalex":"https://openalex.org/W1998819761","doi":"https://doi.org/10.1145/1631162.1631165","mag":"1998819761"},"language":"en","primary_location":{"id":"doi:10.1145/1631162.1631165","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1631162.1631165","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-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/A5075969864","display_name":"Y\u00fcn Chi","orcid":"https://orcid.org/0000-0002-8441-3974"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yun Chi","raw_affiliation_strings":["NEC Laboratories America, Cupertino, CA"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America, Cupertino, CA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112853615","display_name":"Xiaodan Song","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaodan Song","raw_affiliation_strings":["Google Inc., Mountain View, CA"],"affiliations":[{"raw_affiliation_string":"Google Inc., Mountain View, CA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112456591","display_name":"Dengyong Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dengyong Zhou","raw_affiliation_strings":["Microsoft Research, Redmond, WA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060044759","display_name":"Koji Hino","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koji Hino","raw_affiliation_strings":["NEC Laboratories America, Cupertino, CA"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America, Cupertino, CA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112455964","display_name":"Belle L. Tseng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Belle L. Tseng","raw_affiliation_strings":["YAHOO! Inc., Santa Clara, CA"],"affiliations":[{"raw_affiliation_string":"YAHOO! Inc., Santa Clara, CA","institution_ids":["https://openalex.org/I4210134091"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5075969864"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.0102,"has_fulltext":false,"cited_by_count":147,"citation_normalized_percentile":{"value":0.97794763,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"3","issue":"4","first_page":"1","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","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"}},"topics":[{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","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/T12761","display_name":"Data Stream Mining Techniques","score":0.9889000058174133,"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":0.9866999983787537,"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":0.8982788324356079},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.6843785643577576},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.6313459873199463},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6169975996017456},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.590346097946167},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.5303789973258972},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5221479535102844},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.517637312412262},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5152154564857483},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.4892723262310028},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.4808862507343292},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.43345141410827637},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42604708671569824},{"id":"https://openalex.org/keywords/consensus-clustering","display_name":"Consensus clustering","score":0.41748183965682983}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8982788324356079},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.6843785643577576},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.6313459873199463},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6169975996017456},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.590346097946167},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.5303789973258972},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5221479535102844},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.517637312412262},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5152154564857483},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.4892723262310028},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.4808862507343292},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.43345141410827637},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42604708671569824},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.41748183965682983}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1631162.1631165","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1631162.1631165","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.159.5903","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.159.5903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.nec-labs.com/~ychi/publication/09tkdd_evolutionary.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W11451522","https://openalex.org/W97594948","https://openalex.org/W1578099820","https://openalex.org/W1975172027","https://openalex.org/W1980435364","https://openalex.org/W1985796418","https://openalex.org/W1986007546","https://openalex.org/W1988020171","https://openalex.org/W2013029404","https://openalex.org/W2013912476","https://openalex.org/W2039750798","https://openalex.org/W2040466507","https://openalex.org/W2080068757","https://openalex.org/W2092124750","https://openalex.org/W2093038664","https://openalex.org/W2095293504","https://openalex.org/W2097876158","https://openalex.org/W2099242680","https://openalex.org/W2100369465","https://openalex.org/W2121947440","https://openalex.org/W2126455177","https://openalex.org/W2129116669","https://openalex.org/W2134089414","https://openalex.org/W2137820941","https://openalex.org/W2139850885","https://openalex.org/W2155640700","https://openalex.org/W2160167256","https://openalex.org/W2165874743","https://openalex.org/W2165922980","https://openalex.org/W2170337404","https://openalex.org/W2170936641","https://openalex.org/W2293546752","https://openalex.org/W3015637930","https://openalex.org/W4235169531"],"related_works":["https://openalex.org/W2550840372","https://openalex.org/W2564198485","https://openalex.org/W3186815950","https://openalex.org/W2288470165","https://openalex.org/W3124860551","https://openalex.org/W2202413591","https://openalex.org/W4388110928","https://openalex.org/W2006080772","https://openalex.org/W2038937869","https://openalex.org/W2309230723"],"abstract_inverted_index":{"Evolutionary":[0],"clustering":[1,13,20,28,63,93,107,115,122,162,179,184],"is":[2,56],"an":[3],"emerging":[4],"research":[5],"area":[6],"essential":[7],"to":[8,111,128,138,152,190,195],"important":[9],"applications":[10],"such":[11],"as":[12],"dynamic":[14],"Web":[15],"and":[16,19,95,98,120,171,192],"blog":[17],"contents":[18],"data":[21,34,173,196],"streams.":[22],"In":[23,65],"evolutionary":[24,77,105,113,159,177],"clustering,":[25],"a":[26,51,167],"good":[27],"result":[29],"should":[30],"fit":[31],"the":[32,43,59,89,104,112,133,149,153,157],"current":[33],"well,":[35],"while":[36,131],"simultaneously":[37],"not":[38,188],"deviate":[39],"too":[40],"dramatically":[41],"from":[42,88],"recent":[44],"history.":[45],"To":[46],"fulfill":[47],"this":[48,66],"dual":[49],"purpose,":[50],"measure":[52,61],"of":[53,62,156,169],"temporal":[54,74],"smoothness":[55,75],"integrated":[57],"in":[58,76],"overall":[60],"quality.":[64],"article,":[67],"we":[68,83,143],"propose":[69,97],"two":[70],"frameworks":[71],"that":[72,124,145,186],"incorporate":[73],"spectral":[78,106,114,178],"clustering.":[79],"For":[80],"both":[81],"frameworks,":[82],"start":[84],"with":[85],"intuitions":[86],"gained":[87],"well-known":[90],"k":[91,160],"-means":[92,161],"problem,":[94],"then":[96],"solve":[99],"corresponding":[100,158],"cost":[101],"functions":[102],"for":[103],"problems.":[108,163],"Our":[109],"solutions":[110,151],"problems":[116],"provide":[117,148,181],"more":[118,182],"stable":[119],"consistent":[121],"results":[123,185],"are":[125,136,187],"less":[126],"sensitive":[127,189],"short-term":[129],"noises":[130],"at":[132],"same":[134],"time":[135],"adaptive":[137],"long-term":[139],"cluster":[140],"drifts.":[141,197],"Furthermore,":[142],"demonstrate":[144],"our":[146,176],"methods":[147,180],"optimal":[150],"relaxed":[154],"versions":[155],"Performance":[164],"experiments":[165],"over":[166],"number":[168],"real":[170],"synthetic":[172],"sets":[174],"illustrate":[175],"robust":[183],"noise":[191],"can":[193],"adapt":[194]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":17},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":12},{"year":2012,"cited_by_count":8}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}