{"id":"https://openalex.org/W4402353197","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650698","title":"A Clustering Method with Graph Maximum Decoding Information","display_name":"A Clustering Method with Graph Maximum Decoding Information","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402353197","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650698"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650698","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5068643290","display_name":"Xinrun Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinrun Xu","raw_affiliation_strings":["University of Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108950578","display_name":"Manying Lv","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Manying Lv","raw_affiliation_strings":["University of Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094186585","display_name":"Zhanbiao Lian","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanbiao Lian","raw_affiliation_strings":["University of Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102125953","display_name":"Yurong Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yurong Wu","raw_affiliation_strings":["University of Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026229683","display_name":"Yan Jin","orcid":"https://orcid.org/0000-0001-8956-7684"},"institutions":[{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Yan","raw_affiliation_strings":["Advanced Institute of Big Data (Beijing),Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced Institute of Big Data (Beijing),Beijing,China","institution_ids":["https://openalex.org/I4210096250"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111151970","display_name":"Shan Jiang","orcid":"https://orcid.org/0009-0008-4108-5588"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Jiang","raw_affiliation_strings":["University of Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057841485","display_name":"Zhiming Ding","orcid":"https://orcid.org/0000-0002-2057-5325"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiming Ding","raw_affiliation_strings":["University of Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"105","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9976000189781189,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9976000189781189,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9918000102043152,"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/computer-science","display_name":"Computer science","score":0.7225109338760376},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7156064510345459},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.711269736289978},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4912239909172058},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2554744482040405},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25017109513282776},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.206773579120636}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7225109338760376},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7156064510345459},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.711269736289978},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4912239909172058},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2554744482040405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25017109513282776},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.206773579120636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650698","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10650698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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":37,"referenced_works":["https://openalex.org/W1997108895","https://openalex.org/W2045575714","https://openalex.org/W2063431382","https://openalex.org/W2125847585","https://openalex.org/W2142838865","https://openalex.org/W2208501551","https://openalex.org/W2338678442","https://openalex.org/W2740924709","https://openalex.org/W2807944979","https://openalex.org/W2884799030","https://openalex.org/W2885386724","https://openalex.org/W2952416183","https://openalex.org/W2962703596","https://openalex.org/W2967394380","https://openalex.org/W2990703444","https://openalex.org/W2998685375","https://openalex.org/W3004946360","https://openalex.org/W3011520062","https://openalex.org/W3031519732","https://openalex.org/W3045467356","https://openalex.org/W3082776374","https://openalex.org/W3101709902","https://openalex.org/W3110244220","https://openalex.org/W3138409927","https://openalex.org/W3153872005","https://openalex.org/W3156949926","https://openalex.org/W3157552118","https://openalex.org/W3194186453","https://openalex.org/W3196202912","https://openalex.org/W3202164597","https://openalex.org/W4298334591","https://openalex.org/W4317526207","https://openalex.org/W4386474879","https://openalex.org/W4386972917","https://openalex.org/W4387500997","https://openalex.org/W4391182232","https://openalex.org/W6684093485"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"The":[0],"clustering":[1,32,60,112,134,203],"method":[2,61,93],"based":[3],"on":[4,151],"graph":[5,30,118,122,127],"models":[6],"has":[7],"garnered":[8],"increased":[9],"attention":[10],"for":[11,94],"its":[12,56],"widespread":[13],"applicability":[14],"across":[15],"various":[16],"knowledge":[17,178],"domains.":[18],"Its":[19],"adaptability":[20],"to":[21,37,140,147],"integrate":[22],"seamlessly":[23],"with":[24,34,70,144],"other":[25],"relevant":[26],"applications":[27],"endows":[28],"the":[29,35,48,58,63,67,77,82,111,183],"model-based":[31],"analysis":[33],"ability":[36],"robustly":[38],"extract":[39],"\"natural":[40],"associations\"":[41],"or":[42],"\"graph":[43],"structures\"":[44],"within":[45,98],"datasets,":[46],"facilitating":[47],"modelling":[49],"of":[50,115,185],"relationships":[51],"between":[52,74],"data":[53],"points.":[54],"Despite":[55],"efficacy,":[57],"current":[59],"utilizing":[62],"graph-based":[64,99,202],"model":[65],"overlooks":[66],"uncertainty":[68,142],"associated":[69,143],"random":[71,145],"walk":[72],"access":[73],"nodes":[75],"and":[76,121,192],"embedded":[78],"structural":[79,107],"information":[80,108,139,166,190],"in":[81,187,201],"data.":[83],"To":[84],"address":[85],"this":[86],"gap,":[87],"we":[88],"present":[89],"a":[90,163,198],"novel":[91],"Clustering":[92],"Maximizing":[95],"Decoding":[96],"Information":[97],"models,":[100],"named":[101],"CMDI.":[102],"CMDI":[103,157,170,186],"innovatively":[104],"incorporates":[105],"two-dimensional":[106],"theory":[109],"into":[110],"process,":[113],"consisting":[114],"two":[116],"phases:":[117],"structure":[119],"extraction":[120],"vertex":[123],"partitioning.":[124],"Within":[125],"CMDI,":[126],"partitioning":[128],"is":[129],"reformulated":[130],"as":[131,197],"an":[132],"abstract":[133],"problem,":[135],"leveraging":[136],"maximum":[137],"decoding":[138,165,189],"minimize":[141],"visits":[146],"vertices.":[148],"Empirical":[149],"evaluations":[150],"three":[152],"real-world":[153],"datasets":[154],"demonstrate":[155],"that":[156],"outperforms":[158],"classical":[159],"baseline":[160],"methods,":[161],"exhibiting":[162],"superior":[164],"ratio":[167],"(DI-R).":[168],"Furthermore,":[169],"showcases":[171],"heightened":[172],"efficiency,":[173,194],"particularly":[174],"when":[175],"considering":[176],"prior":[177],"(PK).":[179],"These":[180],"findings":[181],"underscore":[182],"effectiveness":[184],"enhancing":[188],"quality":[191],"computational":[193],"positioning":[195],"it":[196],"valuable":[199],"tool":[200],"analyses.":[204]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
