{"id":"https://openalex.org/W2977982705","doi":"https://doi.org/10.1109/ijcnn.2019.8851806","title":"Hybrid K-Means and Improved Self-Adaptive Particle Swarm Optimization for Data Clustering","display_name":"Hybrid K-Means and Improved Self-Adaptive Particle Swarm Optimization for Data Clustering","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977982705","doi":"https://doi.org/10.1109/ijcnn.2019.8851806","mag":"2977982705"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851806","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5044613127","display_name":"Luciano D. S. Pac\u00edfico","orcid":"https://orcid.org/0000-0001-6945-3612"},"institutions":[{"id":"https://openalex.org/I62921916","display_name":"Universidade Federal Rural de Pernambuco","ror":"https://ror.org/02ksmb993","country_code":"BR","type":"education","lineage":["https://openalex.org/I62921916"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Luciano D. S. Pacifico","raw_affiliation_strings":["Departamento de Computacao - DC, Universidade Federal Rural de Pernambuco - UFRPE, Recife, Pernambuco, Brazil"],"affiliations":[{"raw_affiliation_string":"Departamento de Computacao - DC, Universidade Federal Rural de Pernambuco - UFRPE, Recife, Pernambuco, Brazil","institution_ids":["https://openalex.org/I62921916"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025550530","display_name":"Teresa B. Ludermir","orcid":"https://orcid.org/0000-0002-8980-6742"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Teresa B. Ludermir","raw_affiliation_strings":["Centro de Informatica - CIn, Universidade Federal de Pernambuco - UFPE, Recife, Pernambuco, Brazil"],"affiliations":[{"raw_affiliation_string":"Centro de Informatica - CIn, Universidade Federal de Pernambuco - UFPE, Recife, Pernambuco, Brazil","institution_ids":["https://openalex.org/I25112270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044613127"],"corresponding_institution_ids":["https://openalex.org/I62921916"],"apc_list":null,"apc_paid":null,"fwci":1.1201,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.84037743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"18","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9995999932289124,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9995999932289124,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9972000122070312,"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/T10057","display_name":"Face and Expression Recognition","score":0.9878000020980835,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8287992477416992},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.7702364921569824},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6637883186340332},{"id":"https://openalex.org/keywords/crossover","display_name":"Crossover","score":0.640949547290802},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.6302703022956848},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.555294394493103},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5133268237113953},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4791692793369293},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4689730405807495},{"id":"https://openalex.org/keywords/multi-swarm-optimization","display_name":"Multi-swarm optimization","score":0.44271060824394226},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.40763941407203674},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.369428813457489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34718623757362366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20868903398513794}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8287992477416992},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.7702364921569824},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6637883186340332},{"id":"https://openalex.org/C122507166","wikidata":"https://www.wikidata.org/wiki/Q628906","display_name":"Crossover","level":2,"score":0.640949547290802},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.6302703022956848},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.555294394493103},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5133268237113953},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4791692793369293},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4689730405807495},{"id":"https://openalex.org/C122357587","wikidata":"https://www.wikidata.org/wiki/Q6934508","display_name":"Multi-swarm optimization","level":3,"score":0.44271060824394226},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40763941407203674},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.369428813457489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34718623757362366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20868903398513794},{"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851806","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"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/W87092222","https://openalex.org/W1565746575","https://openalex.org/W1974758710","https://openalex.org/W1995450389","https://openalex.org/W2012258043","https://openalex.org/W2018085728","https://openalex.org/W2107941094","https://openalex.org/W2110250181","https://openalex.org/W2126554879","https://openalex.org/W2127218421","https://openalex.org/W2143878868","https://openalex.org/W2152195021","https://openalex.org/W2163163936","https://openalex.org/W2543580944","https://openalex.org/W2741471524","https://openalex.org/W2780091594","https://openalex.org/W2810103678","https://openalex.org/W2875445905","https://openalex.org/W2883452066","https://openalex.org/W2897942856","https://openalex.org/W2904250082","https://openalex.org/W3120740533","https://openalex.org/W4235169531","https://openalex.org/W4250503569","https://openalex.org/W4298236029","https://openalex.org/W6678914141","https://openalex.org/W6788247690"],"related_works":["https://openalex.org/W1984463744","https://openalex.org/W2006324351","https://openalex.org/W2965421953","https://openalex.org/W2349895019","https://openalex.org/W2241146626","https://openalex.org/W2116074880","https://openalex.org/W2950423922","https://openalex.org/W2121780490","https://openalex.org/W2137136732","https://openalex.org/W2132113279"],"abstract_inverted_index":{"Data":[0],"Clustering":[1],"has":[2,131],"become":[3],"an":[4,63],"important":[5],"mechanism":[6],"for":[7],"data":[8,106],"exploration":[9],"and":[10,27,70],"understanding.":[11],"K-Means":[12,30],"algorithm":[13,56],"is":[14,32,57],"currently":[15],"one":[16],"of":[17,38,95,121,135],"the":[18,36,88,93,96,119,133,136,142],"most":[19],"popular":[20],"clustering":[21,55,129],"techniques,":[22],"due":[23],"to":[24,45,77,81,127,141],"its":[25],"simplicity":[26],"scalability.":[28],"However,":[29],"performance":[31,94],"highly":[33],"influenced":[34],"by":[35,118],"choice":[37],"initial":[39],"cluster":[40],"centers,":[41],"which":[42,72],"may":[43],"lead":[44],"suboptimal":[46],"solutions.":[47],"In":[48],"this":[49],"paper,":[50],"a":[51,74],"novel":[52],"hybrid":[53],"partitional":[54],"proposed,":[58],"named":[59],"IDKPSOC-k-means,":[60],"based":[61],"on":[62,103],"improved":[64],"self-adaptive":[65],"Particle":[66],"Swarm":[67],"Optimization":[68],"(PSO)":[69],"K-Means,":[71],"uses":[73],"crossover":[75],"operator":[76],"improve":[78],"PSO":[79],"capability":[80],"escape":[82],"from":[83,87,109],"local":[84],"minima":[85],"points":[86],"problem":[89],"space.":[90],"To":[91],"evaluate":[92],"proposed":[97,137],"approach,":[98],"experiments":[99],"have":[100],"been":[101],"performed":[102],"sixteen":[104],"benchmark":[105],"sets":[107],"obtained":[108],"UCI":[110],"Machine":[111],"Learning":[112],"Repository.":[113],"The":[114],"experimental":[115],"evaluation,":[116],"conducted":[117],"use":[120],"Friedman":[122],"hypothesis":[123],"tests":[124],"in":[125,139],"relation":[126,140],"four":[128],"metrics,":[130],"shown":[132],"effectivity":[134],"model":[138],"comparison":[143],"algorithms.":[144]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
