{"id":"https://openalex.org/W4308592512","doi":"https://doi.org/10.1186/s13634-022-00942-8","title":"A parallel ADMM-based convex clustering method","display_name":"A parallel ADMM-based convex clustering method","publication_year":2022,"publication_date":"2022-11-08","ids":{"openalex":"https://openalex.org/W4308592512","doi":"https://doi.org/10.1186/s13634-022-00942-8"},"language":"en","primary_location":{"id":"doi:10.1186/s13634-022-00942-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-022-00942-8","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-022-00942-8","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-022-00942-8","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023365661","display_name":"Lidija Fodor","orcid":"https://orcid.org/0000-0002-8199-7767"},"institutions":[{"id":"https://openalex.org/I170726198","display_name":"University of Novi Sad","ror":"https://ror.org/00xa57a59","country_code":"RS","type":"education","lineage":["https://openalex.org/I170726198"]}],"countries":["RS"],"is_corresponding":true,"raw_author_name":"Lidija Fodor","raw_affiliation_strings":["Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 4, Novi Sad, 21000, Serbia"],"raw_orcid":"https://orcid.org/0000-0002-8199-7767","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 4, Novi Sad, 21000, Serbia","institution_ids":["https://openalex.org/I170726198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070836307","display_name":"Du\u0161an Jakoveti\u0107","orcid":"https://orcid.org/0000-0003-3497-5589"},"institutions":[{"id":"https://openalex.org/I170726198","display_name":"University of Novi Sad","ror":"https://ror.org/00xa57a59","country_code":"RS","type":"education","lineage":["https://openalex.org/I170726198"]}],"countries":["RS"],"is_corresponding":false,"raw_author_name":"Du\u0161an Jakoveti\u0107","raw_affiliation_strings":["Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 4, Novi Sad, 21000, Serbia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 4, Novi Sad, 21000, Serbia","institution_ids":["https://openalex.org/I170726198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039795730","display_name":"Danijela Boberi\u0107 Krsti\u0107ev","orcid":"https://orcid.org/0000-0002-3609-6476"},"institutions":[{"id":"https://openalex.org/I170726198","display_name":"University of Novi Sad","ror":"https://ror.org/00xa57a59","country_code":"RS","type":"education","lineage":["https://openalex.org/I170726198"]}],"countries":["RS"],"is_corresponding":false,"raw_author_name":"Danijela Boberi\u0107 Krsti\u0107ev","raw_affiliation_strings":["Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 4, Novi Sad, 21000, Serbia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 4, Novi Sad, 21000, Serbia","institution_ids":["https://openalex.org/I170726198"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072295933","display_name":"Srdjan \u0160krbi\u0107","orcid":"https://orcid.org/0000-0002-3993-4092"},"institutions":[{"id":"https://openalex.org/I170726198","display_name":"University of Novi Sad","ror":"https://ror.org/00xa57a59","country_code":"RS","type":"education","lineage":["https://openalex.org/I170726198"]}],"countries":["RS"],"is_corresponding":false,"raw_author_name":"Sr\u0111an \u0160krbi\u0107","raw_affiliation_strings":["Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 4, Novi Sad, 21000, Serbia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 4, Novi Sad, 21000, Serbia","institution_ids":["https://openalex.org/I170726198"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023365661"],"corresponding_institution_ids":["https://openalex.org/I170726198"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":0.6642,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.62832959,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"2022","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9940999746322632,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8512600660324097},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7186017036437988},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6274076104164124},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6163090467453003},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.5766395330429077},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.5525925159454346},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4897346496582031},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.4276614189147949},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3766632378101349},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.369268000125885},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2349858283996582},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1891300082206726}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8512600660324097},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7186017036437988},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6274076104164124},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6163090467453003},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.5766395330429077},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.5525925159454346},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4897346496582031},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.4276614189147949},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3766632378101349},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.369268000125885},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2349858283996582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1891300082206726},{"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1186/s13634-022-00942-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-022-00942-8","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-022-00942-8","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:zenodo.org:7379988","is_oa":true,"landing_page_url":"https://zenodo.org/record/7379988","pdf_url":"https://zenodo.org/record/7379988","source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing 108","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:85928b9025fe4aa5b0b29b12a57362d6","is_oa":true,"landing_page_url":"https://doaj.org/article/85928b9025fe4aa5b0b29b12a57362d6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2022, Iss 1, Pp 1-33 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s13634-022-00942-8","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s13634-022-00942-8","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1186/s13634-022-00942-8","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5299999713897705,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4308592512.pdf","grobid_xml":"https://content.openalex.org/works/W4308592512.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1168671556","https://openalex.org/W1491391313","https://openalex.org/W1977541023","https://openalex.org/W1980317569","https://openalex.org/W2007861510","https://openalex.org/W2021454718","https://openalex.org/W2027987815","https://openalex.org/W2096765209","https://openalex.org/W2097747115","https://openalex.org/W2127597613","https://openalex.org/W2132070206","https://openalex.org/W2138019504","https://openalex.org/W2140514146","https://openalex.org/W2142458924","https://openalex.org/W2150593711","https://openalex.org/W2166624956","https://openalex.org/W2168036472","https://openalex.org/W2492794003","https://openalex.org/W2559655401","https://openalex.org/W2907466675","https://openalex.org/W2949561674","https://openalex.org/W2963966702","https://openalex.org/W3036128815","https://openalex.org/W3104927725","https://openalex.org/W3196550007","https://openalex.org/W4292363360","https://openalex.org/W4308592512","https://openalex.org/W6601487527"],"related_works":["https://openalex.org/W2375684291","https://openalex.org/W3204184292","https://openalex.org/W2354676191","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W3188646203","https://openalex.org/W2596692027","https://openalex.org/W4295724548","https://openalex.org/W2549189808","https://openalex.org/W3153752017"],"abstract_inverted_index":{"Abstract":[0],"Convex":[1],"clustering":[2,18,50,96],"has":[3],"received":[4],"recently":[5],"an":[6],"increased":[7],"interest":[8],"as":[9,21],"a":[10,28,66,88,93,112,153],"valuable":[11],"method":[12,59],"for":[13,80,92,100,137,169],"unsupervised":[14],"learning.":[15],"Unlike":[16],"conventional":[17],"methods":[19],"such":[20],"k-means,":[22],"its":[23,120],"formulation":[24,99],"corresponds":[25],"to":[26,47,106],"solving":[27],"convex":[29,49,81,95,128,170],"optimization":[30],"problem":[31],"and":[32,36,75,78,117,148,158],"hence,":[33],"alleviates":[34],"initialization":[35],"local":[37],"minima":[38],"problems.":[39],"However,":[40],"while":[41],"several":[42],"algorithms":[43,77],"have":[44],"been":[45],"proposed":[46],"solve":[48],"formulations,":[51],"including":[52],"those":[53],"based":[54],"on":[55,71,125,133,146],"the":[56,104,161],"alternating":[57],"direction":[58],"of":[60,69,114,156,160],"multipliers":[61],"(ADMM),":[62],"there":[63],"is":[64],"currently":[65],"limited":[67],"body":[68],"work":[70],"developing":[72],"scalable":[73],"parallel":[74],"distributed":[76],"solvers":[79,168],"clustering.":[82,129,171],"In":[83],"this":[84],"paper,":[85],"we":[86,118],"develop":[87],"parallel,":[89],"ADMM-based":[90,127],"method,":[91,162],"modified":[94],"sum-of-norms":[97],"(SON)":[98],"master\u2013worker":[101],"architectures,":[102],"where":[103],"data":[105,150],"be":[107],"clustered":[108],"are":[109],"partitioned":[110],"across":[111],"number":[113],"worker":[115],"nodes,":[116],"provide":[119],"efficient,":[121],"open-source":[122],"implementation":[123],"(available":[124],"Parallel":[126],"https://github.com/lidijaf/Parallel-ADMM-based-convex-clustering":[130],".":[131],"Accessed":[132],"10":[134],"June":[135],"2022)":[136],"high-performance":[138],"computing":[139],"(HPC)":[140],"cluster":[141],"environments.":[142],"Extensive":[143],"numerical":[144],"evaluations":[145],"real":[147],"synthetic":[149],"sets":[151],"demonstrate":[152],"high":[154],"degree":[155],"scalability":[157],"efficiency":[159],"when":[163],"compared":[164],"with":[165],"existing":[166],"alternative":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
