{"id":"https://openalex.org/W4391092959","doi":"https://doi.org/10.1109/bigdata59044.2023.10386405","title":"Speeding Up Unsupervised Learning Through Early Stopping","display_name":"Speeding Up Unsupervised Learning Through Early Stopping","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391092959","doi":"https://doi.org/10.1109/bigdata59044.2023.10386405"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386405","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","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/A5027098213","display_name":"Andris Do\u00e7aj","orcid":null},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andris Docaj","raw_affiliation_strings":["Texas Tech University,Computer Science Department,Lubbock,TX,USA","Computer Science Department, Texas Tech University, Lubbock, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas Tech University,Computer Science Department,Lubbock,TX,USA","institution_ids":["https://openalex.org/I12315562"]},{"raw_affiliation_string":"Computer Science Department, Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028690312","display_name":"Yu Zhuang","orcid":"https://orcid.org/0000-0003-2717-1777"},"institutions":[{"id":"https://openalex.org/I12315562","display_name":"Texas Tech University","ror":"https://ror.org/0405mnx93","country_code":"US","type":"education","lineage":["https://openalex.org/I12315562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Zhuang","raw_affiliation_strings":["Texas Tech University,Computer Science Department,Lubbock,TX,USA","Computer Science Department, Texas Tech University, Lubbock, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas Tech University,Computer Science Department,Lubbock,TX,USA","institution_ids":["https://openalex.org/I12315562"]},{"raw_affiliation_string":"Computer Science Department, Texas Tech University, Lubbock, TX, USA","institution_ids":["https://openalex.org/I12315562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I12315562"],"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":"1","issue":null,"first_page":"4597","last_page":"4605"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9997000098228455,"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":0.9997000098228455,"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/T12761","display_name":"Data Stream Mining Techniques","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9979000091552734,"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":0.8391249179840088},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.7599338293075562},{"id":"https://openalex.org/keywords/early-stopping","display_name":"Early stopping","score":0.6969780325889587},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6548373699188232},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6533451080322266},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4974043667316437},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4498450458049774},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43104618787765503},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4270590543746948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39080342650413513},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3533974885940552},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3395266532897949},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09274876117706299}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8391249179840088},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.7599338293075562},{"id":"https://openalex.org/C5465570","wikidata":"https://www.wikidata.org/wiki/Q5326898","display_name":"Early stopping","level":3,"score":0.6969780325889587},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6548373699188232},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6533451080322266},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4974043667316437},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4498450458049774},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43104618787765503},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4270590543746948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39080342650413513},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3533974885940552},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3395266532897949},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09274876117706299},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386405","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata59044.2023.10386405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W118481696","https://openalex.org/W1507901999","https://openalex.org/W1524787014","https://openalex.org/W1595303882","https://openalex.org/W1651093245","https://openalex.org/W1742512077","https://openalex.org/W1996344812","https://openalex.org/W2010656511","https://openalex.org/W2048239613","https://openalex.org/W2053171205","https://openalex.org/W2058063871","https://openalex.org/W2085487226","https://openalex.org/W2085757724","https://openalex.org/W2089497633","https://openalex.org/W2120435692","https://openalex.org/W2150593711","https://openalex.org/W2153233077","https://openalex.org/W2169013230","https://openalex.org/W2587094051","https://openalex.org/W3012123778","https://openalex.org/W3195139144","https://openalex.org/W6611373715","https://openalex.org/W6637231022","https://openalex.org/W6678914141","https://openalex.org/W6907478538"],"related_works":["https://openalex.org/W2381926679","https://openalex.org/W2378211422","https://openalex.org/W2007009951","https://openalex.org/W2082644203","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2350539780","https://openalex.org/W2130974462","https://openalex.org/W3165040664","https://openalex.org/W3122652148"],"abstract_inverted_index":{"Ever-increasing":[0],"amounts":[1],"of":[2,6,15,22,27,60,82,108,145,173,182,202,230,236,238,260,287,307,311],"data":[3],"make":[4],"labeling":[5],"datasets":[7],"uneconomic.":[8],"Unsupervised":[9],"learning,":[10],"specifically":[11],"the":[12,41,58,61,80,89,92,101,109,119,125,140,146,154,170,174,180,200,211,215,219,257,265,298,327],"K-Means":[13,32,43,54,62,103,141],"family":[14],"clustering":[16,46,75,131,157,206,276,308],"algorithms,":[17],"provide":[18,293],"a":[19,65,135,294],"pragmatic":[20],"way":[21],"dealing":[23],"with":[24,34,233,244,280,320],"sizable":[25],"quantities":[26],"unlabeled":[28],"data.":[29],"The":[30,143,223,270],"bisecting":[31,53,102],"algorithm":[33,63,104],"refinement":[35],"has":[36],"been":[37],"shown":[38],"to":[39,68,129,164,249,256,302],"outperform":[40],"regular":[42],"in":[44],"both":[45],"speed":[47,69,207],"and":[48,127,150,166,193,208,267,284,313],"quality.":[49,132,158,209,289],"Past":[50],"works":[51,87],"on":[52,205],"revealed":[55],"that":[56,118,274],"limiting":[57],"iterations":[59],"is":[64,122,162],"promising":[66,295],"approach":[67],"up":[70],"calculations":[71],"while":[72],"still":[73],"maintaining":[74],"quality":[76],"as":[77,95,218],"measured":[78],"by":[79,124],"sum":[81],"square":[83],"errors":[84],"(SSE).":[85],"Particular":[86],"from":[88],"past":[90],"employed":[91],"centroid":[93,120],"difference":[94,121],"an":[96,234],"iteration-stopping":[97],"condition":[98],"when":[99],"using":[100,194,264],"or":[105],"some":[106],"forms":[107],"fuzzy":[110],"C-Means":[111],"algorithm.":[112],"In":[113,247],"this":[114,321],"paper,":[115],"we":[116,185,251],"show":[117],"affected":[123],"dataset":[126,315],"leads":[128],"unreliable":[130],"We":[133],"propose":[134],"single-parameter":[136],"criterion":[137,147,300],"for":[138,297],"stopping":[139,176,221,255],"iterations.":[142],"parameter":[144,191,204],"employs":[148],"SSE,":[149],"its":[151,303],"selection":[152],"manages":[153],"desired":[155],"final":[156],"Moreover,":[159],"our":[160,183,203,245],"method":[161,225],"easy":[163],"implement":[165],"does":[167],"not":[168],"carry":[169],"dataset-dependent":[171],"issues":[172],"centroid-difference-based":[175],"condition.":[177,222],"To":[178],"examine":[179],"effectiveness":[181],"method,":[184],"performed":[186,213],"various":[187],"experiments":[188,212],"under":[189],"diverse":[190],"values":[192],"multiple":[195],"datasets.":[196],"Our":[197,290],"studies":[198],"tested":[199],"effects":[201],"All":[210],"used":[214],"no-membership":[216],"change":[217],"benchmark":[220],"early-stopping":[224,299],"results":[226,271],"reveal":[227,273],"speed-up":[228,282],"factors":[229,283],"over":[231],"200,":[232],"increase":[235],"SSE":[237],"only":[239],"about":[240],"three":[241],"percent":[242],"compared":[243],"benchmark.":[246],"addition":[248],"this,":[250],"also":[252],"applied":[253],"early":[254],"automatic":[258],"determination":[259],"optimal":[261,275],"cluster":[262,288],"numbers":[263],"Calinski-Harabasz":[266],"Ray-Turi":[268],"Indices.":[269],"obtained":[272,279],"can":[277,323],"be":[278,324],"impactful":[281],"minimal":[285],"loss":[286],"experimental":[291],"findings":[292],"path":[296],"due":[301],"computational":[304],"reduction,":[305],"preservation":[306],"quality,":[309],"ease":[310],"use,":[312],"broad":[314],"applicability.":[316],"Sample":[317],"code":[318],"associated":[319],"paper":[322],"found":[325],"at":[326],"following":[328],"link,":[329],"https://github.com/adocaj/code.":[330]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}