{"id":"https://openalex.org/W2954737966","doi":"https://doi.org/10.1109/tnnls.2019.2919723","title":"Incremental Local Distribution-Based Clustering Using Bayesian Adaptive Resonance Theory","display_name":"Incremental Local Distribution-Based Clustering Using Bayesian Adaptive Resonance Theory","publication_year":2019,"publication_date":"2019-06-26","ids":{"openalex":"https://openalex.org/W2954737966","doi":"https://doi.org/10.1109/tnnls.2019.2919723","mag":"2954737966","pmid":"https://pubmed.ncbi.nlm.nih.gov/31251200"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2019.2919723","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2019.2919723","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 and Learning Systems","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/A5100398729","display_name":"Ling Wang","orcid":"https://orcid.org/0000-0003-4098-7906"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Wang","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4098-7906","affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100644864","display_name":"Hui Zhu","orcid":"https://orcid.org/0000-0002-6042-2438"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Zhu","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6042-2438","affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101057127","display_name":"Jianyao Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianyao Meng","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022113595","display_name":"Wei He","orcid":"https://orcid.org/0000-0002-8944-9861"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei He","raw_affiliation_strings":["School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8944-9861","affiliations":[{"raw_affiliation_string":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":null,"apc_paid":null,"fwci":1.2707,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.85015132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"30","issue":"11","first_page":"3496","last_page":"3504"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9991999864578247,"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":0.9991999864578247,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9973000288009644,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9921000003814697,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8281986713409424},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6765284538269043},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.6311736702919006},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.5684459805488586},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5604861974716187},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5536462068557739},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5085819959640503},{"id":"https://openalex.org/keywords/adaptive-resonance-theory","display_name":"Adaptive resonance theory","score":0.49617037177085876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4623813033103943},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44886600971221924},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38282641768455505},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32674288749694824}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8281986713409424},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6765284538269043},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.6311736702919006},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.5684459805488586},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5604861974716187},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5536462068557739},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5085819959640503},{"id":"https://openalex.org/C115755159","wikidata":"https://www.wikidata.org/wiki/Q352487","display_name":"Adaptive resonance theory","level":3,"score":0.49617037177085876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4623813033103943},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44886600971221924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38282641768455505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32674288749694824},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2019.2919723","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2019.2919723","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 and Learning Systems","raw_type":"journal-article"},{"id":"pmid:31251200","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31251200","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 and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1192688618","display_name":"\u57fa\u4e8e\u589e\u91cf\u5b66\u4e60\u7684\u6f14\u5316\u6a21\u7cca\u7cfb\u7edf\u7684\u89e3\u91ca\u6027\u95ee\u9898\u7814\u7a76","funder_award_id":"61572073","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6850434380","display_name":null,"funder_award_id":"2017YFB0306403","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W86660359","https://openalex.org/W1552448920","https://openalex.org/W1921624739","https://openalex.org/W1967764809","https://openalex.org/W1982601383","https://openalex.org/W1988812547","https://openalex.org/W2014014359","https://openalex.org/W2020166044","https://openalex.org/W2030263248","https://openalex.org/W2033996602","https://openalex.org/W2034171445","https://openalex.org/W2043637184","https://openalex.org/W2045657291","https://openalex.org/W2076665520","https://openalex.org/W2085645322","https://openalex.org/W2108136843","https://openalex.org/W2126994539","https://openalex.org/W2128012710","https://openalex.org/W2272985318","https://openalex.org/W2299778380","https://openalex.org/W2326490218","https://openalex.org/W2376293991","https://openalex.org/W2388585476","https://openalex.org/W2396830449","https://openalex.org/W2461673454","https://openalex.org/W2523089212","https://openalex.org/W2526347192","https://openalex.org/W2531230844","https://openalex.org/W2534248263","https://openalex.org/W2589681188","https://openalex.org/W2767740307","https://openalex.org/W3147742554","https://openalex.org/W4236122429","https://openalex.org/W6657923742"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W3144143113","https://openalex.org/W4306940721","https://openalex.org/W3022637481","https://openalex.org/W3120229345","https://openalex.org/W2160785859","https://openalex.org/W2491448268","https://openalex.org/W2738096727","https://openalex.org/W2374506950","https://openalex.org/W3174322327"],"abstract_inverted_index":{"Most":[0],"of":[1,36],"the":[2,10,14,27,37,52,80,95,100,104],"existing":[3],"Bayesian":[4,20,53],"clustering":[5,21,49,130,144],"algorithms":[6,22,145],"perform":[7],"well":[8,128],"on":[9,114],"balanced":[11],"data.":[12],"When":[13],"data":[15,84,97,117,132],"are":[16],"highly":[17],"imbalanced,":[18],"these":[19],"tend":[23],"to":[24,62,65,99],"strongly":[25],"favor":[26],"larger":[28],"clusters,":[29,81],"but":[30,91],"provide":[31],"a":[32,66,87,137],"notably":[33],"low":[34],"detection":[35],"smaller":[38],"clusters.":[39,105],"In":[40],"this":[41],"paper,":[42],"we":[43],"present":[44],"an":[45],"incremental":[46],"local":[47],"distribution-based":[48],"algorithm":[50,59,75,110,126],"with":[51,86,111],"adaptive":[54],"resonance":[55],"theory":[56],"(ILBART).":[57],"This":[58],"is":[60],"developed":[61],"adapt":[63],"itself":[64],"changing":[67],"environment":[68],"without":[69],"using":[70],"any":[71],"predefined":[72],"parameters.":[73],"The":[74,119],"not":[76],"only":[77],"accurately":[78],"finds":[79],"even":[82],"in":[83,146],"sets":[85],"severely":[88],"imbalanced":[89,116,131],"distribution,":[90],"also":[92,135],"efficiently":[93],"processes":[94],"dynamic":[96],"according":[98],"evolving":[101],"relationships":[102],"among":[103],"We":[106],"test":[107],"our":[108,124],"proposed":[109,125],"experiments":[112],"conducted":[113],"several":[115,147],"sets.":[118],"experimental":[120],"results":[121],"show":[122],"that":[123],"performs":[127],"for":[129],"and":[133],"can":[134],"obtain":[136],"better":[138],"performance":[139,148],"than":[140],"many":[141],"other":[142],"relevant":[143],"indices.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
