{"id":"https://openalex.org/W4318185309","doi":"https://doi.org/10.1109/bigdata55660.2022.10020324","title":"Semi-supervised Embedding for Scalable and Accurate Time Series Clustering","display_name":"Semi-supervised Embedding for Scalable and Accurate Time Series Clustering","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318185309","doi":"https://doi.org/10.1109/bigdata55660.2022.10020324"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020324","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5079062994","display_name":"Andrew Hill","orcid":"https://orcid.org/0000-0001-5468-6616"},"institutions":[{"id":"https://openalex.org/I921990950","display_name":"University of Colorado Denver","ror":"https://ror.org/02hh7en24","country_code":"US","type":"education","lineage":["https://openalex.org/I921990950"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Hill","raw_affiliation_strings":["University of Colorado Denver,Department of Computer Science and Engineering,Denver,CO,USA","Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Colorado Denver,Department of Computer Science and Engineering,Denver,CO,USA","institution_ids":["https://openalex.org/I921990950"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, USA","institution_ids":["https://openalex.org/I921990950"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017665414","display_name":"Russell P. Bowler","orcid":"https://orcid.org/0000-0003-4651-363X"},"institutions":[{"id":"https://openalex.org/I3019586173","display_name":"National Jewish Health","ror":"https://ror.org/016z2bp30","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019586173"]},{"id":"https://openalex.org/I921990950","display_name":"University of Colorado Denver","ror":"https://ror.org/02hh7en24","country_code":"US","type":"education","lineage":["https://openalex.org/I921990950"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Russell Bowler","raw_affiliation_strings":["National Jewish Health,Denver,CO,USA","National Jewish Health, Denver, CO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Jewish Health,Denver,CO,USA","institution_ids":["https://openalex.org/I3019586173","https://openalex.org/I921990950"]},{"raw_affiliation_string":"National Jewish Health, Denver, CO, USA","institution_ids":["https://openalex.org/I3019586173","https://openalex.org/I921990950"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022404035","display_name":"Katerina Kechris","orcid":"https://orcid.org/0000-0002-3725-5459"},"institutions":[{"id":"https://openalex.org/I921990950","display_name":"University of Colorado Denver","ror":"https://ror.org/02hh7en24","country_code":"US","type":"education","lineage":["https://openalex.org/I921990950"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Katerina Kechris","raw_affiliation_strings":["University of Colorado Denver—Anschutz,Department of Biostatistics and Informatics,Aurora,CO,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Colorado Denver—Anschutz,Department of Biostatistics and Informatics,Aurora,CO,USA","institution_ids":["https://openalex.org/I921990950"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007232242","display_name":"Farnoush Banaei\u2010Kashani","orcid":"https://orcid.org/0000-0003-4102-9873"},"institutions":[{"id":"https://openalex.org/I921990950","display_name":"University of Colorado Denver","ror":"https://ror.org/02hh7en24","country_code":"US","type":"education","lineage":["https://openalex.org/I921990950"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Farnoush Banaei-Kashani","raw_affiliation_strings":["University of Colorado Denver,Department of Computer Science and Engineering,Denver,CO,USA","Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Colorado Denver,Department of Computer Science and Engineering,Denver,CO,USA","institution_ids":["https://openalex.org/I921990950"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, USA","institution_ids":["https://openalex.org/I921990950"]}]}],"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":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"942","last_page":"951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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/T11309","display_name":"Music and Audio Processing","score":0.9872999787330627,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9817000031471252,"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/autoencoder","display_name":"Autoencoder","score":0.8319389820098877},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7871357202529907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7202364206314087},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.638238787651062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.623895525932312},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6040816903114319},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5648599863052368},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5377585887908936},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.487416535615921},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4759654402732849},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4638628363609314},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4386264383792877},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4353001117706299},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.42920413613319397},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.41879743337631226},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36152786016464233},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2396562695503235},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.0994606614112854}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8319389820098877},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7871357202529907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7202364206314087},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.638238787651062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.623895525932312},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6040816903114319},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5648599863052368},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5377585887908936},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.487416535615921},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4759654402732849},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4638628363609314},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4386264383792877},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4353001117706299},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.42920413613319397},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.41879743337631226},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36152786016464233},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2396562695503235},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0994606614112854},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020324","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1959608418","https://openalex.org/W1974339580","https://openalex.org/W1987971958","https://openalex.org/W2037537012","https://openalex.org/W2078559879","https://openalex.org/W2091921805","https://openalex.org/W2098759488","https://openalex.org/W2101234009","https://openalex.org/W2108323654","https://openalex.org/W2296629652","https://openalex.org/W2532798634","https://openalex.org/W2556467266","https://openalex.org/W2622816133","https://openalex.org/W2734777338","https://openalex.org/W2787035179","https://openalex.org/W2951736998","https://openalex.org/W2969974905","https://openalex.org/W3085482458","https://openalex.org/W4235169531","https://openalex.org/W6604828220","https://openalex.org/W6631190155","https://openalex.org/W6639103823","https://openalex.org/W6640963894","https://openalex.org/W6735236233","https://openalex.org/W6751395999","https://openalex.org/W6757925885","https://openalex.org/W6766628391","https://openalex.org/W6766893349"],"related_works":["https://openalex.org/W3148060700","https://openalex.org/W2794908468","https://openalex.org/W2531570999","https://openalex.org/W2553312496","https://openalex.org/W2042184006","https://openalex.org/W4319302697","https://openalex.org/W2921630034","https://openalex.org/W4390062853","https://openalex.org/W2062926306","https://openalex.org/W4310801741"],"abstract_inverted_index":{"While":[0],"time":[1,13,51,57,90],"series":[2,14,52,58,91],"data":[3,26,49,77],"are":[4,16,68],"abundant":[5],"in":[6,93],"numerous":[7],"real":[8],"world":[9],"applications,":[10],"large":[11,30],"labeled":[12,25,114],"datasets":[15,135],"scarce.":[17],"Semi-supervised":[18],"models,":[19],"which":[20],"leverage":[21,108],"small":[22,111],"amounts":[23],"of":[24,32,64,113,121,134,144,164,185],"along":[27],"with":[28],"a":[29,81,110,132,145,157,166,182],"set":[31],"unlabeled":[33,48],"data,":[34],"have":[35],"been":[36],"shown":[37],"to":[38,70],"significantly":[39,117],"outperform":[40],"unsupervised":[41,171],"learning":[42,72,87],"models":[43],"that":[44,107,137,153],"only":[45,109],"rely":[46],"on":[47,131],"for":[50,89,127],"clustering.":[53,128],"However,":[54],"existing":[55,189],"semi-supervised":[56,86,104,190],"clustering":[59,92,147],"algorithms":[60],"suffer":[61],"from":[62],"lack":[63],"scalability":[65],"as":[66],"they":[67],"limited":[69],"perform":[71],"operations":[73],"within":[74],"the":[75,94,119,122],"original":[76],"space.":[78,96],"We":[79,151],"propose":[80],"scalable":[82],"and":[83],"accurate":[84],"autoencoder-based":[85],"model":[88],"embedded":[95],"With":[97],"this":[98],"model,":[99],"we":[100],"also":[101,180],"introduce":[102],"multiple":[103],"objective":[105],"functions":[106],"number":[112],"examples":[115],"but":[116],"improve":[118,142],"quality":[120],"autoencoder\u2019s":[123],"learned":[124],"latent":[125],"space":[126],"Our":[129],"experiments":[130],"variety":[133],"show":[136],"our":[138,154,177],"methods":[139,155,179],"can":[140],"often":[141],"performance":[143],"typical":[146],"method":[148],"(namely,":[149],"k-means).":[150],"demonstrate":[152],"achieve":[156,181],"maximum":[158,183],"average":[159],"Adjusted":[160],"Rand":[161],"Index":[162],"(ARI)":[163],"0.897,":[165],"140%":[167],"increase":[168],"over":[169,187],"an":[170,188],"Convolutional":[172],"Autoencoder":[173],"(CAE)":[174],"model.":[175,191],"Finally,":[176],"proposed":[178],"improvement":[184],"44%":[186]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}