{"id":"https://openalex.org/W2197242498","doi":"https://doi.org/10.1109/eusipco.2015.7362892","title":"On generative models for sequential formation of clusters","display_name":"On generative models for sequential formation of clusters","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W2197242498","doi":"https://doi.org/10.1109/eusipco.2015.7362892","mag":"2197242498"},"language":"en","primary_location":{"id":"doi:10.1109/eusipco.2015.7362892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eusipco.2015.7362892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 23rd European Signal Processing Conference (EUSIPCO)","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/A5006962534","display_name":"Petar M. Djuri\u0107","orcid":"https://orcid.org/0000-0001-7791-3199"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Petar M. Djuric","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006750969","display_name":"Kezi Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kezi Yu","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5006962534"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":0.8629,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.82717292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2786","last_page":"2790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998999834060669,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998999834060669,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9520000219345093,"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/T11152","display_name":"Stochastic processes and statistical mechanics","score":0.9221000075340271,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dirichlet-process","display_name":"Dirichlet process","score":0.7359004616737366},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.7177901268005371},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6416776180267334},{"id":"https://openalex.org/keywords/hierarchical-dirichlet-process","display_name":"Hierarchical Dirichlet process","score":0.6384910345077515},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.6299309134483337},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6038213968276978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5293776988983154},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.528449296951294},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5235031247138977},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5076838731765747},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.4523410201072693},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4366978406906128},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4206516742706299},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.4176154136657715},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41728031635284424},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3817526698112488},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3382088541984558},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25865137577056885},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.14874690771102905},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10282477736473083},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.07596433162689209}],"concepts":[{"id":"https://openalex.org/C2781280628","wikidata":"https://www.wikidata.org/wiki/Q5280766","display_name":"Dirichlet process","level":3,"score":0.7359004616737366},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.7177901268005371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6416776180267334},{"id":"https://openalex.org/C141318989","wikidata":"https://www.wikidata.org/wiki/Q5753066","display_name":"Hierarchical Dirichlet process","level":4,"score":0.6384910345077515},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.6299309134483337},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6038213968276978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5293776988983154},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.528449296951294},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5235031247138977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5076838731765747},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.4523410201072693},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4366978406906128},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4206516742706299},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.4176154136657715},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41728031635284424},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3817526698112488},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3382088541984558},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25865137577056885},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.14874690771102905},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10282477736473083},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.07596433162689209},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eusipco.2015.7362892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eusipco.2015.7362892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 23rd European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1271758","https://openalex.org/W43029633","https://openalex.org/W64187236","https://openalex.org/W258053484","https://openalex.org/W748467075","https://openalex.org/W1483307070","https://openalex.org/W1511747216","https://openalex.org/W1663973292","https://openalex.org/W2038885294","https://openalex.org/W2069429561","https://openalex.org/W2072644219","https://openalex.org/W2091797506","https://openalex.org/W2117853077","https://openalex.org/W2121194706","https://openalex.org/W2241296481","https://openalex.org/W2491497334","https://openalex.org/W2797808779","https://openalex.org/W4308951891","https://openalex.org/W6600050727","https://openalex.org/W6602662854","https://openalex.org/W6678060785","https://openalex.org/W6690332741"],"related_works":["https://openalex.org/W2914864478","https://openalex.org/W2008338582","https://openalex.org/W2097627380","https://openalex.org/W4291700620","https://openalex.org/W1999586157","https://openalex.org/W22044811","https://openalex.org/W2625329765","https://openalex.org/W2766840109","https://openalex.org/W2255612897","https://openalex.org/W2955328590"],"abstract_inverted_index":{"In":[0,80],"the":[1,22,48,55,89,94],"literature":[2],"of":[3,8,24,34,47,66,91,93,102,119],"machine":[4],"learning,":[5],"a":[6,31,61],"class":[7],"unsupervised":[9],"approaches":[10,19],"is":[11,53,60],"based":[12],"on":[13],"Dirichlet":[14,49,95],"process":[15,50,96],"mixture":[16,51],"models.":[17],"These":[18],"fall":[20],"into":[21],"category":[23],"nonparametric":[25],"Bayesian":[26],"methods,":[27],"and":[28,42],"they":[29],"find":[30],"wide":[32],"range":[33],"applications":[35],"including":[36],"in":[37],"biology,":[38],"computer":[39],"science,":[40],"engineering,":[41],"finance.":[43],"An":[44],"important":[45],"assumption":[46],"models":[52,86,112],"that":[54,87],"data":[56,67],"are":[57],"exchangeable.":[58],"This":[59],"restriction":[62,90],"for":[63,100],"many":[64],"types":[65],"whose":[68],"structures":[69],"vary":[70],"over":[71],"time":[72],"or":[73,75],"space":[74],"some":[76],"other":[77],"independent":[78],"variables.":[79],"this":[81],"paper,":[82],"we":[83],"address":[84,109],"generative":[85],"remove":[88],"exchangeability":[92],"model,":[97],"which":[98],"allows":[99],"creation":[101],"mixtures":[103],"with":[104],"time-varying":[105],"structures.":[106],"We":[107],"also":[108],"how":[110],"these":[111],"can":[113],"be":[114],"applied":[115],"to":[116],"sequential":[117],"estimation":[118],"clusters.":[120]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
