{"id":"https://openalex.org/W2170337404","doi":"https://doi.org/10.1145/1281192.1281212","title":"Evolutionary spectral clustering by incorporating temporal smoothness","display_name":"Evolutionary spectral clustering by incorporating temporal smoothness","publication_year":2007,"publication_date":"2007-08-12","ids":{"openalex":"https://openalex.org/W2170337404","doi":"https://doi.org/10.1145/1281192.1281212","mag":"2170337404"},"language":"en","primary_location":{"id":"doi:10.1145/1281192.1281212","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1281192.1281212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5075969864","display_name":"Y\u00fcn Chi","orcid":"https://orcid.org/0000-0002-8441-3974"},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yun Chi","raw_affiliation_strings":["NEC Laboratories America"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America","institution_ids":["https://openalex.org/I4210107353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112853615","display_name":"Xiaodan Song","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaodan Song","raw_affiliation_strings":["NEC Laboratories America"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America","institution_ids":["https://openalex.org/I4210107353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112456591","display_name":"Dengyong Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dengyong Zhou","raw_affiliation_strings":["NEC Laboratories America"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America","institution_ids":["https://openalex.org/I4210107353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060044759","display_name":"Koji Hino","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Koji Hino","raw_affiliation_strings":["NEC Laboratories America"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America","institution_ids":["https://openalex.org/I4210107353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112455964","display_name":"Belle L. Tseng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Belle L. Tseng","raw_affiliation_strings":["NEC Laboratories America"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories America","institution_ids":["https://openalex.org/I4210107353"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5075969864"],"corresponding_institution_ids":["https://openalex.org/I4210107353"],"apc_list":null,"apc_paid":null,"fwci":28.0508,"has_fulltext":false,"cited_by_count":369,"citation_normalized_percentile":{"value":0.996404,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"153","last_page":"162"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9945999979972839,"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":0.9945999979972839,"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":0.9846000075340271,"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":0.9736999869346619,"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":0.8989884853363037},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.7042773365974426},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6456059813499451},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.6326088905334473},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.6193022131919861},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5647950172424316},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.5553356409072876},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5035542845726013},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.4976866543292999},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.46554434299468994},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.45005345344543457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43886151909828186},{"id":"https://openalex.org/keywords/consensus-clustering","display_name":"Consensus clustering","score":0.4375864565372467},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.43114137649536133}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8989884853363037},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.7042773365974426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6456059813499451},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.6326088905334473},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.6193022131919861},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5647950172424316},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.5553356409072876},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5035542845726013},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.4976866543292999},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.46554434299468994},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.45005345344543457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43886151909828186},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.4375864565372467},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.43114137649536133}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1281192.1281212","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1281192.1281212","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.216.3871","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.216.3871","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/07kdd_evolutionary.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W11451522","https://openalex.org/W97594948","https://openalex.org/W1578099820","https://openalex.org/W1975172027","https://openalex.org/W1986007546","https://openalex.org/W2013912476","https://openalex.org/W2080068757","https://openalex.org/W2093038664","https://openalex.org/W2099242680","https://openalex.org/W2100369465","https://openalex.org/W2121947440","https://openalex.org/W2126455177","https://openalex.org/W2129116669","https://openalex.org/W2133576408","https://openalex.org/W2134089414","https://openalex.org/W2137137397","https://openalex.org/W2137820941","https://openalex.org/W2139850885","https://openalex.org/W2160167256","https://openalex.org/W2165874743","https://openalex.org/W2170936641","https://openalex.org/W2293546752","https://openalex.org/W2798909945","https://openalex.org/W4243187700","https://openalex.org/W4249507550"],"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/W2006080772","https://openalex.org/W2202413591","https://openalex.org/W4388110928","https://openalex.org/W2038937869","https://openalex.org/W2309230723"],"abstract_inverted_index":{"Evolutionary":[0],"clustering":[1,13,20,28,63,92,106,114,121,160,177,182],"is":[2,56],"an":[3],"emerging":[4],"research":[5],"area":[6],"essential":[7],"to":[8,110,127,137,151,188,193],"important":[9],"applications":[10],"such":[11],"as":[12],"dynamic":[14],"Web":[15],"and":[16,19,94,97,119,169,190],"blog":[17],"contents":[18],"data":[21,34,171,194],"streams.":[22],"In":[23,65],"evolutionary":[24,77,104,112,158,175],"clustering,":[25],"a":[26,51,165],"good":[27],"result":[29],"should":[30],"fit":[31],"the":[32,43,59,89,103,111,132,148,152,156],"current":[33],"well,":[35],"while":[36,130],"simultaneously":[37],"not":[38,186],"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":[53,62,155,167],"temporal":[54,74],"smoothness":[55,75],"integrated":[57],"in":[58,76],"overall":[60],"quality.":[64],"paper,":[67],"we":[68,83,142],"propose":[69,96],"two":[70],"frameworks":[71],"that":[72,123,144,184],"incorporate":[73],"spectral":[78,105,113,176],"clustering.":[79],"For":[80],"both":[81],"frameworks,":[82],"start":[84],"with":[85],"intuitions":[86],"gained":[87],"well-known":[90],"k-means":[91,159],"problem,":[93],"then":[95],"solve":[98],"corresponding":[99,157],"cost":[100],"functions":[101],"for":[102],"problems.":[107,161],"Our":[108],"solutions":[109,150],"problems":[115],"provide":[116,147,179],"more":[117,180],"stable":[118],"consistent":[120],"results":[122,183],"are":[124,135,185],"less":[125],"sensitive":[126,187],"short-term":[128],"noises":[129],"at":[131],"same":[133],"time":[134],"adaptive":[136],"long-term":[138],"cluster":[139],"drifts.":[140,195],"Furthermore,":[141],"demonstrate":[143],"our":[145,174],"methods":[146,178],"optimal":[149],"relaxed":[153],"versions":[154],"Performance":[162],"experiments":[163],"over":[164],"number":[166],"real":[168],"synthetic":[170],"sets":[172],"illustrate":[173],"robust":[181],"noise":[189],"can":[191],"adapt":[192]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":17},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":29},{"year":2017,"cited_by_count":29},{"year":2016,"cited_by_count":23},{"year":2015,"cited_by_count":23},{"year":2014,"cited_by_count":36},{"year":2013,"cited_by_count":31},{"year":2012,"cited_by_count":26}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
