{"id":"https://openalex.org/W3093611504","doi":"https://doi.org/10.4018/ijdwm.2020100105","title":"An Improvement of K-Medoids Clustering Algorithm Based on Fixed Point Iteration","display_name":"An Improvement of K-Medoids Clustering Algorithm Based on Fixed Point Iteration","publication_year":2020,"publication_date":"2020-10-01","ids":{"openalex":"https://openalex.org/W3093611504","doi":"https://doi.org/10.4018/ijdwm.2020100105","mag":"3093611504"},"language":"en","primary_location":{"id":"doi:10.4018/ijdwm.2020100105","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijdwm.2020100105","pdf_url":null,"source":{"id":"https://openalex.org/S53932126","display_name":"International Journal of Data Warehousing and Mining","issn_l":"1548-3924","issn":["1548-3924","1548-3932"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Warehousing and Mining","raw_type":"journal-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/A5075860077","display_name":"Xiaodi Huang","orcid":"https://orcid.org/0000-0001-6530-2462"},"institutions":[{"id":"https://openalex.org/I39774598","display_name":"Hefei University","ror":"https://ror.org/01f5rdf64","country_code":"CN","type":"education","lineage":["https://openalex.org/I39774598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodi Huang","raw_affiliation_strings":["Hefei University, China"],"raw_orcid":"https://orcid.org/0000-0001-6530-2462","affiliations":[{"raw_affiliation_string":"Hefei University, China","institution_ids":["https://openalex.org/I39774598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043281628","display_name":"Minglun Ren","orcid":"https://orcid.org/0000-0001-5107-2133"},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minglun Ren","raw_affiliation_strings":["Hefei University of Technology, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0001-5107-2133","affiliations":[{"raw_affiliation_string":"Hefei University of Technology, Hefei, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027697010","display_name":"Zhongfeng Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I39774598","display_name":"Hefei University","ror":"https://ror.org/01f5rdf64","country_code":"CN","type":"education","lineage":["https://openalex.org/I39774598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongfeng Hu","raw_affiliation_strings":["Hefei University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hefei University, China","institution_ids":["https://openalex.org/I39774598"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2686,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6701403,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"16","issue":"4","first_page":"84","last_page":"94"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9983999729156494,"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":0.9983999729156494,"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":0.9952999949455261,"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/T10057","display_name":"Face and Expression Recognition","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/medoid","display_name":"Medoid","score":0.9161065816879272},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.775076150894165},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7145463228225708},{"id":"https://openalex.org/keywords/k-medoids","display_name":"k-medoids","score":0.7005435824394226},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6210887432098389},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4736170470714569},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.46671006083488464},{"id":"https://openalex.org/keywords/iterative-and-incremental-development","display_name":"Iterative and incremental development","score":0.45460978150367737},{"id":"https://openalex.org/keywords/fixed-point","display_name":"Fixed point","score":0.41627076268196106},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.33921578526496887},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.33883965015411377},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33366456627845764},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.28794634342193604},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2559688687324524},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12122029066085815}],"concepts":[{"id":"https://openalex.org/C63085389","wikidata":"https://www.wikidata.org/wiki/Q4287912","display_name":"Medoid","level":3,"score":0.9161065816879272},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.775076150894165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7145463228225708},{"id":"https://openalex.org/C65932786","wikidata":"https://www.wikidata.org/wiki/Q3191282","display_name":"k-medoids","level":5,"score":0.7005435824394226},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6210887432098389},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4736170470714569},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.46671006083488464},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.45460978150367737},{"id":"https://openalex.org/C61445026","wikidata":"https://www.wikidata.org/wiki/Q217608","display_name":"Fixed point","level":2,"score":0.41627076268196106},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.33921578526496887},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33883965015411377},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33366456627845764},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.28794634342193604},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2559688687324524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12122029066085815},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/ijdwm.2020100105","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijdwm.2020100105","pdf_url":null,"source":{"id":"https://openalex.org/S53932126","display_name":"International Journal of Data Warehousing and Mining","issn_l":"1548-3924","issn":["1548-3924","1548-3932"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Warehousing and Mining","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:jdwm00:v:16:y:2020:i:4:p:84-94","is_oa":false,"landing_page_url":"https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.2020100105","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1490245427","https://openalex.org/W1501500081","https://openalex.org/W1739732693","https://openalex.org/W1987401219","https://openalex.org/W1996802345","https://openalex.org/W2068617723","https://openalex.org/W2083620785","https://openalex.org/W2094247338","https://openalex.org/W2132614631","https://openalex.org/W2144614420","https://openalex.org/W2165835468","https://openalex.org/W2302722331","https://openalex.org/W2384361563","https://openalex.org/W2441072774","https://openalex.org/W2501069295","https://openalex.org/W2526108798","https://openalex.org/W2532102307","https://openalex.org/W2593620947","https://openalex.org/W2757782259","https://openalex.org/W2758138366","https://openalex.org/W2759884981","https://openalex.org/W2794121917","https://openalex.org/W2805528009","https://openalex.org/W2805555771"],"related_works":["https://openalex.org/W3133849001","https://openalex.org/W1481798977","https://openalex.org/W2380053301","https://openalex.org/W4225615903","https://openalex.org/W2143329214","https://openalex.org/W2758138366","https://openalex.org/W2605300748","https://openalex.org/W2088480663","https://openalex.org/W2967990367","https://openalex.org/W2367316505"],"abstract_inverted_index":{"The":[0,73,117],"process":[1],"of":[2,61,103,112,133,163],"K-medoids":[3],"algorithm":[4,48,135],"is":[5,49,107,119,136],"that":[6,129],"it":[7],"first":[8],"selects":[9],"data":[10,36,56],"randomly":[11],"as":[12],"initial":[13,17],"centers":[14,27,84,106],"to":[15,37,79],"form":[16],"clusters.":[18,164],"Then,":[19],"based":[20],"on":[21,121],"PAM":[22,47],"(partitioning":[23],"around":[24],"medoids)":[25],"algorithm,":[26],"will":[28,69,149],"be":[29,150],"sequential":[30],"replaced":[31],"by":[32],"all":[33],"the":[34,42,55,59,76,81,101,110,126,130,141,146],"remaining":[35],"find":[38],"a":[39,87,160],"result":[40,127],"has":[41],"best":[43],"inherent":[44],"convergence.":[45],"Since":[46],"an":[50],"iterative":[51],"ergodic":[52],"strategy,":[53],"when":[54],"size":[57],"or":[58,159],"number":[60,162],"clusters":[62],"are":[63],"huge,":[64],"its":[65,71],"expensive":[66],"computational":[67],"overhead":[68],"hinder":[70],"feasibility.":[72],"authors":[74],"use":[75],"fixed-point":[77],"iteration":[78],"search":[80],"optimal":[82,105],"clustering":[83,131,147],"and":[85,125],"build":[86],"FPK-medoids":[88],"(fixed":[89],"point-based":[90],"K-medoids)":[91],"algorithm.":[92,143],"By":[93],"constructing":[94],"fixed":[95],"point":[96],"equations":[97],"for":[98],"each":[99],"cluster,":[100],"problem":[102],"searching":[104],"converted":[108],"into":[109],"solving":[111],"equation":[113],"set":[114],"in":[115,153],"parallel.":[116],"experiment":[118],"carried":[120],"six":[122],"standard":[123],"datasets,":[124],"shows":[128],"efficiency":[132],"proposed":[134],"significantly":[137],"improved":[138],"compared":[139],"with":[140,156],"conventional":[142],"In":[144],"addition,":[145],"quality":[148],"markedly":[151],"enhanced":[152],"handling":[154],"problems":[155],"large-scale":[157],"datasets":[158],"large":[161]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-08T07:41:36.138363","created_date":"2025-10-10T00:00:00"}