{"id":"https://openalex.org/W3174452951","doi":"https://doi.org/10.1109/access.2021.3091397","title":"Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms","display_name":"Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3174452951","doi":"https://doi.org/10.1109/access.2021.3091397","mag":"3174452951"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3091397","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3091397","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09461801.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09461801.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067987333","display_name":"Behnaz Merikhi","orcid":"https://orcid.org/0000-0002-3441-2949"},"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":true,"raw_author_name":"Behnaz Merikhi","raw_affiliation_strings":["Concordia University, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103172344","display_name":"M. Reza Soleymani","orcid":"https://orcid.org/0000-0002-4913-833X"},"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":"M. R. Soleymani","raw_affiliation_strings":["Concordia University, Montreal, QC, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University, Montreal, QC, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5067987333"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.0994,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.89246661,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"9","issue":null,"first_page":"93703","last_page":"93722"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.996999979019165,"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":0.996999979019165,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9940999746322632,"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"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9919000267982483,"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":0.8215310573577881},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6950711011886597},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.5340626835823059},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5026445388793945},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.4854755699634552},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4487537145614624},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.44603776931762695},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.4434855580329895},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44307276606559753},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26092374324798584},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19161775708198547}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8215310573577881},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6950711011886597},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.5340626835823059},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5026445388793945},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.4854755699634552},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4487537145614624},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.44603776931762695},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.4434855580329895},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44307276606559753},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26092374324798584},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19161775708198547},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3091397","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3091397","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09461801.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8b8ada48894d4d2ea4fd68f19aa58670","is_oa":true,"landing_page_url":"https://doaj.org/article/8b8ada48894d4d2ea4fd68f19aa58670","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 93703-93722 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3091397","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3091397","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09461801.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3174452951.pdf","grobid_xml":"https://content.openalex.org/works/W3174452951.grobid-xml"},"referenced_works_count":79,"referenced_works":["https://openalex.org/W147012890","https://openalex.org/W414544266","https://openalex.org/W971229228","https://openalex.org/W1531910981","https://openalex.org/W1547841921","https://openalex.org/W1558758677","https://openalex.org/W1576123718","https://openalex.org/W1585002319","https://openalex.org/W1594924988","https://openalex.org/W1673310716","https://openalex.org/W1970351663","https://openalex.org/W1974758710","https://openalex.org/W1978225003","https://openalex.org/W1996344812","https://openalex.org/W1999075329","https://openalex.org/W2000621750","https://openalex.org/W2006620109","https://openalex.org/W2016944307","https://openalex.org/W2044721770","https://openalex.org/W2044775998","https://openalex.org/W2051224630","https://openalex.org/W2052556921","https://openalex.org/W2053677366","https://openalex.org/W2057691548","https://openalex.org/W2073459066","https://openalex.org/W2076408892","https://openalex.org/W2089040534","https://openalex.org/W2090365396","https://openalex.org/W2096166399","https://openalex.org/W2108028245","https://openalex.org/W2108031918","https://openalex.org/W2112627135","https://openalex.org/W2117638283","https://openalex.org/W2118771192","https://openalex.org/W2127971792","https://openalex.org/W2130593958","https://openalex.org/W2135940344","https://openalex.org/W2138810473","https://openalex.org/W2140190241","https://openalex.org/W2153233077","https://openalex.org/W2156773695","https://openalex.org/W2159945133","https://openalex.org/W2162404506","https://openalex.org/W2170007150","https://openalex.org/W2182722412","https://openalex.org/W2219898335","https://openalex.org/W2327178873","https://openalex.org/W2399495295","https://openalex.org/W2479927430","https://openalex.org/W2521957736","https://openalex.org/W2522502682","https://openalex.org/W2543580944","https://openalex.org/W2576474906","https://openalex.org/W2740924709","https://openalex.org/W2767919612","https://openalex.org/W2771081342","https://openalex.org/W2788714147","https://openalex.org/W2809086467","https://openalex.org/W2904250082","https://openalex.org/W2965415593","https://openalex.org/W2966207845","https://openalex.org/W2972260659","https://openalex.org/W2989283265","https://openalex.org/W2996095076","https://openalex.org/W3002566578","https://openalex.org/W3015917763","https://openalex.org/W3021268733","https://openalex.org/W3098905329","https://openalex.org/W3120740533","https://openalex.org/W3151310069","https://openalex.org/W4230817896","https://openalex.org/W4241582122","https://openalex.org/W4249545506","https://openalex.org/W4252447396","https://openalex.org/W6637131181","https://openalex.org/W6668990524","https://openalex.org/W6679921305","https://openalex.org/W6712855567","https://openalex.org/W6812330276"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W3144143113","https://openalex.org/W4306940721","https://openalex.org/W3022637481","https://openalex.org/W2160785859","https://openalex.org/W2892323093","https://openalex.org/W4301002638","https://openalex.org/W2491448268","https://openalex.org/W3120229345","https://openalex.org/W2374506950"],"abstract_inverted_index":{"Cluster":[0],"analysis":[1],"using":[2,50,60],"metaheuristic":[3],"algorithms":[4,19,62,149],"has":[5,128,156],"earned":[6],"increasing":[7],"popularity":[8],"over":[9,131],"recent":[10],"years":[11],"due":[12],"to":[13,82,113,160,222,274],"the":[14,39,46,79,95,102,110,116,151,184,195,209,216,219,223,243,249,275,286,289],"great":[15],"success":[16],"of":[17,41,94,97,135,179,183,218,236,283,288],"these":[18],"in":[20,24,173,177,201,212,238,248,256,270],"finding":[21],"high-quality":[22],"clusters":[23,43,98,112,185,206,284],"complex":[25],"real-world":[26],"problems.":[27],"This":[28],"paper":[29],"proposes":[30],"a":[31,65,74,132,231,239,280],"novel":[32],"framework":[33,221],"for":[34,78,115,150],"automatic":[35,170],"data":[36,246],"clustering":[37,59,88,165,171],"with":[38,44,58,101,121,207],"capability":[40],"generating":[42],"approximately":[45,208],"same":[47,210,250],"maximum":[48],"distortion":[49,117,211],"nature-inspired":[51],"binary":[52,75,147,225],"optimization":[53,148],"algorithms.":[54],"The":[55,86,124,191,234],"inherent":[56],"problem":[57],"such":[61,252],"is":[63,198,227],"having":[64,279],"huge":[66],"search":[67,175],"space.":[68],"Therefore,":[69],"we":[70],"have":[71],"also":[72,157,228],"proposed":[73,87,125,196,220,276,290],"encoding":[76],"scheme":[77],"particle":[80],"representation":[81],"alleviate":[83],"this":[84],"problem.":[85],"solution":[89,197,277],"requires":[90],"no":[91],"prior":[92],"knowledge":[93],"number":[96,282],"and":[99,108,138,163,167,181,204,263,268,278],"proceed":[100],"process":[103],"based":[104],"on":[105],"re-clustering,":[106],"merging,":[107],"modifying":[109],"small":[111],"compensate":[114],"gap":[118],"between":[119,245],"groups":[120],"different":[122,146],"sizes.":[123],"framework's":[126],"performance":[127],"been":[129,158],"evaluated":[130],"wide":[133],"range":[134],"synthetic,":[136],"real-life,":[137],"higher":[139],"dimensional":[140],"datasets":[141],"first":[142],"by":[143,186],"considering":[144],"four":[145],"optimizer":[152],"module.":[153],"Then,":[154],"it":[155],"compared":[159],"multiple":[161],"classical":[162],"new":[164],"solutions":[166],"two":[168],"other":[169],"techniques":[172],"continuous":[174],"space":[176],"terms":[178],"separation":[180],"compactness":[182],"utilizing":[187],"internal":[188],"validity":[189],"measures.":[190],"experimental":[192],"results":[193,241],"show":[194],"highly":[199],"efficient":[200],"creating":[202],"well-separated":[203],"compact":[205],"most":[213],"datasets.":[214],"Moreover,":[215],"application":[217],"correlated":[224],"dataset":[226,240],"reported":[229],"as":[230,253],"case":[232],"study.":[233],"presence":[235],"correlation":[237],"from":[242],"similarity":[244],"points":[247],"category,":[251],"repeated":[254],"measurements":[255],"remote":[257],"sensing,":[258],"crowdsourced":[259],"multi-view":[260],"video":[261],"uploading,":[262],"augmented":[264],"reality.":[265],"Simplicity,":[266],"customizability,":[267],"flexibility":[269],"adding":[271],"extra":[272],"conditions":[273],"dynamic":[281],"are":[285],"advantages":[287],"framework.":[291]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
