{"id":"https://openalex.org/W2101980876","doi":"https://doi.org/10.1109/tnn.2007.2000051","title":"Centroid Neural Network With a Divergence Measure for GPDF Data Clustering","display_name":"Centroid Neural Network With a Divergence Measure for GPDF Data Clustering","publication_year":2008,"publication_date":"2008-05-29","ids":{"openalex":"https://openalex.org/W2101980876","doi":"https://doi.org/10.1109/tnn.2007.2000051","mag":"2101980876","pmid":"https://pubmed.ncbi.nlm.nih.gov/18541496"},"language":"en","primary_location":{"id":"doi:10.1109/tnn.2007.2000051","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnn.2007.2000051","pdf_url":null,"source":{"id":"https://openalex.org/S42080949","display_name":"IEEE Transactions on Neural Networks","issn_l":"1045-9227","issn":["1045-9227","1941-0093"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5022326567","display_name":"Dong-Chul Park","orcid":"https://orcid.org/0009-0007-7442-2301"},"institutions":[{"id":"https://openalex.org/I89440247","display_name":"Myongji University","ror":"https://ror.org/00s9dpb54","country_code":"KR","type":"education","lineage":["https://openalex.org/I89440247"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dong-Chul Park","raw_affiliation_strings":["Department of Information Engineering, Myong Ji University, Yong In, KyungKi-do 449-728, Korea. parkd@dreamwiz.com","Dept. of Inf. Eng., Myong Ji Univ., Yongin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, Myong Ji University, Yong In, KyungKi-do 449-728, Korea. parkd@dreamwiz.com","institution_ids":["https://openalex.org/I89440247"]},{"raw_affiliation_string":"Dept. of Inf. Eng., Myong Ji Univ., Yongin","institution_ids":["https://openalex.org/I89440247"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101848967","display_name":"Oh\u2010Hyun Kwon","orcid":"https://orcid.org/0000-0003-2060-1340"},"institutions":[{"id":"https://openalex.org/I89440247","display_name":"Myongji University","ror":"https://ror.org/00s9dpb54","country_code":"KR","type":"education","lineage":["https://openalex.org/I89440247"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Oh-Hyun Kwon","raw_affiliation_strings":["Department of Information Engineering, Myongji University, Yongin si, Gyeonggi, South Korea","[Department of Information Eng., Myongji University, Yongin si, Gyeonggi, South Korea]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, Myongji University, Yongin si, Gyeonggi, South Korea","institution_ids":["https://openalex.org/I89440247"]},{"raw_affiliation_string":"[Department of Information Eng., Myongji University, Yongin si, Gyeonggi, South Korea]","institution_ids":["https://openalex.org/I89440247"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005118639","display_name":"Jio Chung","orcid":null},"institutions":[{"id":"https://openalex.org/I89440247","display_name":"Myongji University","ror":"https://ror.org/00s9dpb54","country_code":"KR","type":"education","lineage":["https://openalex.org/I89440247"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jio Chung","raw_affiliation_strings":["Department of Information Engineering, Myongji University, Yongin si, Gyeonggi, South Korea","[Department of Information Eng., Myongji University, Yongin si, Gyeonggi, South Korea]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, Myongji University, Yongin si, Gyeonggi, South Korea","institution_ids":["https://openalex.org/I89440247"]},{"raw_affiliation_string":"[Department of Information Eng., Myongji University, Yongin si, Gyeonggi, South Korea]","institution_ids":["https://openalex.org/I89440247"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I89440247"],"apc_list":null,"apc_paid":null,"fwci":3.923,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.92605932,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"19","issue":"6","first_page":"948","last_page":"957"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9972000122070312,"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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9972000122070312,"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"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9970999956130981,"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/T10320","display_name":"Neural Networks and Applications","score":0.9970999956130981,"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/codebook","display_name":"Codebook","score":0.9194495677947998},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7224397659301758},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.7028356790542603},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6807957291603088},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.6556183099746704},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6483002305030823},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6314482688903809},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.6260531544685364},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.589659571647644},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5848707556724548},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.49798035621643066},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4750288128852844},{"id":"https://openalex.org/keywords/neural-gas","display_name":"Neural gas","score":0.44540631771087646},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.41865837574005127},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.23021593689918518},{"id":"https://openalex.org/keywords/time-delay-neural-network","display_name":"Time delay neural network","score":0.19559332728385925}],"concepts":[{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.9194495677947998},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7224397659301758},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.7028356790542603},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6807957291603088},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.6556183099746704},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6483002305030823},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6314482688903809},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.6260531544685364},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.589659571647644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5848707556724548},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.49798035621643066},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4750288128852844},{"id":"https://openalex.org/C90322556","wikidata":"https://www.wikidata.org/wiki/Q1981169","display_name":"Neural gas","level":4,"score":0.44540631771087646},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.41865837574005127},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.23021593689918518},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.19559332728385925},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008390","descriptor_name":"Markov Chains","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008390","descriptor_name":"Markov Chains","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008390","descriptor_name":"Markov Chains","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D010364","descriptor_name":"Pattern Recognition, Visual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010364","descriptor_name":"Pattern Recognition, Visual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010364","descriptor_name":"Pattern Recognition, Visual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D021641","descriptor_name":"Recognition, Psychology","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D021641","descriptor_name":"Recognition, Psychology","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D021641","descriptor_name":"Recognition, Psychology","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D046709","descriptor_name":"Pattern Recognition, Physiological","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D046709","descriptor_name":"Pattern Recognition, Physiological","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D046709","descriptor_name":"Pattern Recognition, Physiological","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/tnn.2007.2000051","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnn.2007.2000051","pdf_url":null,"source":{"id":"https://openalex.org/S42080949","display_name":"IEEE Transactions on Neural Networks","issn_l":"1045-9227","issn":["1045-9227","1941-0093"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks","raw_type":"journal-article"},{"id":"pmid:18541496","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/18541496","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W11045727","https://openalex.org/W943204654","https://openalex.org/W1560013842","https://openalex.org/W1592898276","https://openalex.org/W1981735114","https://openalex.org/W2001059870","https://openalex.org/W2006533296","https://openalex.org/W2098773974","https://openalex.org/W2099120078","https://openalex.org/W2101275224","https://openalex.org/W2108343180","https://openalex.org/W2108450058","https://openalex.org/W2112235833","https://openalex.org/W2116244604","https://openalex.org/W2116862910","https://openalex.org/W2116952749","https://openalex.org/W2118324213","https://openalex.org/W2124521482","https://openalex.org/W2129244720","https://openalex.org/W2136179852","https://openalex.org/W2137005420","https://openalex.org/W2145997363","https://openalex.org/W2158709883","https://openalex.org/W2161209569","https://openalex.org/W2167188741","https://openalex.org/W3148637803","https://openalex.org/W4236088841"],"related_works":["https://openalex.org/W2148772884","https://openalex.org/W2017514583","https://openalex.org/W2100120615","https://openalex.org/W2352648934","https://openalex.org/W1929869830","https://openalex.org/W2017401491","https://openalex.org/W2387054321","https://openalex.org/W2012827167","https://openalex.org/W2062765737","https://openalex.org/W2039369709"],"abstract_inverted_index":{"An":[0],"unsupervised":[1,29,104],"competitive":[2,30],"neural":[3,31,37,105],"network":[4,38],"for":[5,59,69,163,191],"efficient":[6],"clustering":[7,66],"of":[8,15,53,81,152,160],"Gaussian":[9],"probability":[10],"density":[11,17],"function":[12],"(GPDF)":[13],"data":[14,119,134],"continuous":[16],"hidden":[18],"Markov":[19],"models":[20],"(CDHMMs)":[21],"is":[22],"proposed":[23,28,89],"in":[24,56,85,188],"this":[25,189],"paper.":[26],"The":[27],"network,":[32],"called":[33],"the":[34,41,50,57,64,70,78,82,86,88,93,96,107,115,130,150,153,155,158],"divergence-based":[35,176,181],"centroid":[36],"(DCNN),":[39],"employs":[40],"divergence":[42],"measure":[43,47],"as":[44,145],"its":[45],"distance":[46],"and":[48,95,179],"utilizes":[49,91],"statistical":[51],"characteristics":[52],"observation":[54,83],"densities":[55,84],"HMM":[58],"speech":[60],"recognition":[61,143,170],"problems.":[62],"While":[63],"conventional":[65,103],"algorithms":[67],"used":[68,162],"vector":[71],"quantization":[72],"(VQ)":[73],"codebook":[74],"design":[75],"utilize":[76],"only":[77],"mean":[79,94],"values":[80],"HMM,":[87],"DCNN":[90,108,156],"both":[92],"covariance":[97],"values.":[98],"When":[99,138],"compared":[100],"with":[101,174],"other":[102],"networks,":[106],"successfully":[109],"allocates":[110,125],"more":[111],"code":[112,127,164],"vectors":[113,128,165],"to":[114,129,140,148],"regions":[116,131],"where":[117,132],"GPDF":[118,133],"are":[120,135,185],"densely":[121],"distributed":[122],"while":[123,168],"it":[124],"fewer":[126],"sparsely":[136],"distributed.":[137],"applied":[139],"Korean":[141],"monophone":[142],"problems":[144],"a":[146,175,180,192],"tool":[147],"reduce":[149],"size":[151],"codebook,":[154],"reduced":[157],"number":[159],"GPDFs":[161],"by":[166],"65.3%":[167],"preserving":[169],"accuracy.":[171],"Experimental":[172],"results":[173],"k-means":[177],"algorithm":[178,184],"self-organizing":[182],"map":[183],"also":[186],"presented":[187],"paper":[190],"performance":[193],"comparison.":[194]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
