{"id":"https://openalex.org/W3006840371","doi":"https://doi.org/10.1109/ssci44817.2019.9003076","title":"Efficient Computation of Log-likelihood Function in Clustering Overdispersed Count Data Using Multinomial Beta-Liouville Distribution","display_name":"Efficient Computation of Log-likelihood Function in Clustering Overdispersed Count Data Using Multinomial Beta-Liouville Distribution","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3006840371","doi":"https://doi.org/10.1109/ssci44817.2019.9003076","mag":"3006840371"},"language":"en","primary_location":{"id":"doi:10.1109/ssci44817.2019.9003076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci44817.2019.9003076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5028683493","display_name":"Masoud Daghyani","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Masoud Daghyani","raw_affiliation_strings":["Department of Electrical and Computer Engineering (ECE), Concordia University, Montreal, QC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering (ECE), Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060719711","display_name":"Nuha Zamzami","orcid":"https://orcid.org/0000-0001-9328-9218"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nuha Zamzami","raw_affiliation_strings":["Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090600716","display_name":"Nizar Bouguila","orcid":"https://orcid.org/0000-0001-7224-7940"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nizar Bouguila","raw_affiliation_strings":["Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2892,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.68965121,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"78","issue":null,"first_page":"986","last_page":"993"},"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9983000159263611,"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/T11106","display_name":"Data Management and Algorithms","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/multinomial-distribution","display_name":"Multinomial distribution","score":0.7186506986618042},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6295537352561951},{"id":"https://openalex.org/keywords/count-data","display_name":"Count data","score":0.5479981899261475},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.5409257411956787},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4579426646232605},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4577729403972626},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3761284351348877},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2846331000328064},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.11344635486602783}],"concepts":[{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.7186506986618042},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6295537352561951},{"id":"https://openalex.org/C33643355","wikidata":"https://www.wikidata.org/wiki/Q5176731","display_name":"Count data","level":3,"score":0.5479981899261475},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.5409257411956787},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4579426646232605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4577729403972626},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3761284351348877},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2846331000328064},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.11344635486602783},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci44817.2019.9003076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci44817.2019.9003076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W67881473","https://openalex.org/W1517637854","https://openalex.org/W1566135517","https://openalex.org/W1573666180","https://openalex.org/W1601375977","https://openalex.org/W1621412722","https://openalex.org/W1625255723","https://openalex.org/W1973628995","https://openalex.org/W1981367467","https://openalex.org/W1989796759","https://openalex.org/W2000833653","https://openalex.org/W2017814585","https://openalex.org/W2031453508","https://openalex.org/W2049633694","https://openalex.org/W2050627451","https://openalex.org/W2070248766","https://openalex.org/W2082503527","https://openalex.org/W2082633197","https://openalex.org/W2083121989","https://openalex.org/W2107034620","https://openalex.org/W2108903064","https://openalex.org/W2113110240","https://openalex.org/W2117812871","https://openalex.org/W2117853077","https://openalex.org/W2129414564","https://openalex.org/W2135631383","https://openalex.org/W2140336868","https://openalex.org/W2143869282","https://openalex.org/W2144245426","https://openalex.org/W2151103935","https://openalex.org/W2153668970","https://openalex.org/W2331518786","https://openalex.org/W2488678869","https://openalex.org/W2536231481","https://openalex.org/W2807567715","https://openalex.org/W2904250115","https://openalex.org/W2912889105","https://openalex.org/W2921645317","https://openalex.org/W2947566949","https://openalex.org/W2967098356","https://openalex.org/W3141897683","https://openalex.org/W3148106702","https://openalex.org/W4232080660","https://openalex.org/W4238346259","https://openalex.org/W4240035482","https://openalex.org/W4322382706","https://openalex.org/W6602738186","https://openalex.org/W6634348268","https://openalex.org/W6636494156","https://openalex.org/W6636549275","https://openalex.org/W6679166005","https://openalex.org/W6681046083"],"related_works":["https://openalex.org/W2115188409","https://openalex.org/W2016318053","https://openalex.org/W1994299254","https://openalex.org/W1556021157","https://openalex.org/W1985440090","https://openalex.org/W2143869282","https://openalex.org/W2070114007","https://openalex.org/W4388295412","https://openalex.org/W1984724218","https://openalex.org/W2087571625"],"abstract_inverted_index":{"In":[0,27],"this":[1],"paper,":[2],"we":[3,92],"present":[4],"an":[5],"overdispersed":[6],"count":[7,132],"data":[8,133],"clustering":[9],"algorithm,":[10,39],"which":[11,40,71,107],"uses":[12],"the":[13,18,22,30,34,37,42,45,54,63,68,94,100,105,137,140],"mesh":[14,38,95],"method":[15],"for":[16,98,131],"computing":[17,99],"log-likelihood":[19,50,69],"function,":[20,51],"of":[21,29,36,44,61,67,104,139,146],"multinomial":[23,46],"Beta-Liouville":[24],"distribution":[25],"(MBLD).":[26],"one":[28],"recent":[31],"research":[32],"papers,":[33],"use":[35,85],"involves":[41],"approximation":[43],"Dirichlet":[47],"distribution's":[48],"(MDD)":[49],"based":[52,117],"on":[53,118],"Bernoulli":[55],"polynomials,":[56],"has":[57],"been":[58],"proposed":[59,141],"instead":[60],"using":[62],"traditional":[64],"numerical":[65],"computation":[66],"function":[70,103],"either":[72],"results":[73],"in":[74],"instability,":[75],"or":[76],"leads":[77],"to":[78,126],"long":[79],"run":[80],"times":[81],"that":[82,149],"make":[83],"its":[84],"infeasible":[86],"when":[87],"modeling":[88],"large-scale":[89],"data.":[90],"Therefore,":[91],"extend":[93],"algorithm":[96],"approach":[97],"log":[101],"likelihood":[102],"MBLD,":[106,119],"is":[108,120],"a":[109,128,144],"more":[110],"flexible":[111],"distribution.":[112],"A":[113],"finite":[114],"mixture":[115],"model":[116],"optimized":[121],"by":[122],"expectation-maximization,":[123],"and":[124],"attempts":[125],"achieve":[127],"high":[129],"accuracy":[130],"clustering.":[134],"We":[135],"evaluate":[136],"performance":[138],"approach,":[142],"through":[143],"set":[145],"empirical":[147],"experiments,":[148],"concern":[150],"natural":[151],"scenes":[152],"categorization.":[153]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
