{"id":"https://openalex.org/W1975195692","doi":"https://doi.org/10.1109/icnc.2013.6818103","title":"An improved affinity propagation clustering algorithm for large-scale data sets","display_name":"An improved affinity propagation clustering algorithm for large-scale data sets","publication_year":2013,"publication_date":"2013-07-01","ids":{"openalex":"https://openalex.org/W1975195692","doi":"https://doi.org/10.1109/icnc.2013.6818103","mag":"1975195692"},"language":"en","primary_location":{"id":"doi:10.1109/icnc.2013.6818103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2013.6818103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 Ninth International Conference on Natural Computation (ICNC)","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/A5100684380","display_name":"Xiaonan Liu","orcid":"https://orcid.org/0000-0002-8690-868X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiaonan Liu","raw_affiliation_strings":["State Key Laboratory or Mathematics Engineering and Advanced Computing, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory or Mathematics Engineering and Advanced Computing, Zhengzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076739634","display_name":"Meijuan Yin","orcid":"https://orcid.org/0000-0002-0066-1971"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meijuan Yin","raw_affiliation_strings":["State Key Laboratory or Mathematics Engineering and Advanced Computing, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory or Mathematics Engineering and Advanced Computing, Zhengzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085545916","display_name":"Junyong Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junyong Luo","raw_affiliation_strings":["State Key Laboratory or Mathematics Engineering and Advanced Computing, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory or Mathematics Engineering and Advanced Computing, Zhengzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076619411","display_name":"Wuping Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126530","display_name":"Data Assurance and Communication Security","ror":"https://ror.org/02z2gfm30","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210126530","https://openalex.org/I4210156404"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wuping Chen","raw_affiliation_strings":["Science and Technology on Information Assurance Laboratory, Beijing, China","Science and Technology on Information, Assurance Laboratory, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Information Assurance Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210126530"]},{"raw_affiliation_string":"Science and Technology on Information, Assurance Laboratory, Beijing, China","institution_ids":["https://openalex.org/I4210126530"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100684380"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4945,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.85450411,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"894","last_page":"899"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9950000047683716,"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":0.9950000047683716,"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":0.977400004863739,"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/T10885","display_name":"Gene expression and cancer classification","score":0.975600004196167,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/affinity-propagation","display_name":"Affinity propagation","score":0.9004693627357483},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.9003274440765381},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.7312328815460205},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.6739414930343628},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6723424196243286},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.6622347831726074},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6000180840492249},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.5834170579910278},{"id":"https://openalex.org/keywords/determining-the-number-of-clusters-in-a-data-set","display_name":"Determining the number of clusters in a data set","score":0.5190335512161255},{"id":"https://openalex.org/keywords/single-linkage-clustering","display_name":"Single-linkage clustering","score":0.4962936043739319},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4920022189617157},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4888295531272888},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.45481690764427185},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44578635692596436},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.4435841739177704},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4254623353481293},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.41273748874664307},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26010414958000183},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.05043277144432068}],"concepts":[{"id":"https://openalex.org/C109659709","wikidata":"https://www.wikidata.org/wiki/Q3407504","display_name":"Affinity propagation","level":5,"score":0.9004693627357483},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.9003274440765381},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.7312328815460205},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.6739414930343628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6723424196243286},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.6622347831726074},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6000180840492249},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.5834170579910278},{"id":"https://openalex.org/C149872217","wikidata":"https://www.wikidata.org/wiki/Q5265701","display_name":"Determining the number of clusters in a data set","level":5,"score":0.5190335512161255},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.4962936043739319},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4920022189617157},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4888295531272888},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.45481690764427185},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44578635692596436},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.4435841739177704},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4254623353481293},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.41273748874664307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26010414958000183},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05043277144432068},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc.2013.6818103","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2013.6818103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 Ninth International Conference on Natural Computation (ICNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1618101233","https://openalex.org/W1886306325","https://openalex.org/W1895591946","https://openalex.org/W1987971958","https://openalex.org/W1993236420","https://openalex.org/W2084812512","https://openalex.org/W2103704311","https://openalex.org/W2165232124","https://openalex.org/W2358586643","https://openalex.org/W2949823946","https://openalex.org/W3112020351","https://openalex.org/W6639689668"],"related_works":["https://openalex.org/W2358586643","https://openalex.org/W2245611357","https://openalex.org/W2559422900","https://openalex.org/W2794209582","https://openalex.org/W2188840951","https://openalex.org/W2770741777","https://openalex.org/W2171610853","https://openalex.org/W2361333219","https://openalex.org/W2185849206","https://openalex.org/W2567087402"],"abstract_inverted_index":{"Affinity":[0],"Propagation":[1],"(AP)":[2],"clustering":[3,41,53,117,191,198],"does":[4],"not":[5,22],"need":[6],"to":[7,79,107,127,137],"set":[8,78],"the":[9,40,99,123,148,154,161,177,184,190],"number":[10],"of":[11,43,89,112,132],"clusters,":[12],"and":[13,18,35,139,160,169,180],"has":[14],"advantages":[15],"on":[16,46,72,104,121,167],"efficiency":[17],"accuracy,":[19],"but":[20],"is":[21,60,82,102],"suitable":[23],"for":[24,39],"large-scale":[25,47],"data":[26,48,64,77,134,142,149,158,171],"clustering.":[27],"To":[28],"ensure":[29],"both":[30],"a":[31,36,145,195],"low":[32],"time":[33,192],"complexity":[34],"good":[37],"accuracy":[38],"method":[42],"affinity":[44,57],"propagation":[45,58],"clustering,":[49],"an":[50],"improved":[51],"AP":[52,68,96,100,116,179,182],"algorithm":[54,69,101,186],"named":[55],"hierarchical":[56],"(HAP)":[59],"proposed,":[61],"which":[62,90],"clusters":[63],"points":[65,143,150],"by":[66,95,153],"using":[67],"several":[70,86],"times":[71],"different":[73],"level":[74],"data.":[75],"The":[76,164],"be":[80,92],"clustered":[81,94,152],"firstly":[83],"divided":[84],"into":[85],"subsets,":[87],"each":[88,105,113,157],"can":[91,187],"efficiently":[93,138],"algorithm.":[97],"Then,":[98],"performed":[103],"subset":[106],"respectively":[108],"select":[109,128],"cluster":[110,125,141],"centers":[111,126],"subset.":[114],"Further,":[115],"was":[118],"again":[119],"implemented":[120],"all":[122,147],"local":[124],"well-suited":[129],"global":[130,162],"exemplars":[131],"whole":[133],"set.":[135],"Finally,":[136],"accurately":[140],"in":[144],"large-scale,":[146],"are":[151],"similarities":[155],"between":[156],"point":[159],"exemplars.":[163],"experimental":[165],"results":[166],"real":[168],"simulated":[170],"sets":[172],"show":[173],"that,":[174],"compared":[175],"with":[176,194],"traditional":[178],"adaptive":[181],"algorithm,":[183],"HAP":[185],"greatly":[188],"reduce":[189],"consumption":[193],"relatively":[196],"better":[197],"results.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}