{"id":"https://openalex.org/W4291910533","doi":"https://doi.org/10.1109/tpami.2022.3198411","title":"Multiview Unsupervised Shapelet Learning for Multivariate Time Series Clustering","display_name":"Multiview Unsupervised Shapelet Learning for Multivariate Time Series Clustering","publication_year":2022,"publication_date":"2022-08-15","ids":{"openalex":"https://openalex.org/W4291910533","doi":"https://doi.org/10.1109/tpami.2022.3198411","pmid":"https://pubmed.ncbi.nlm.nih.gov/35969573"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3198411","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","indexed_in":["crossref","pubmed"],"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/A5100641132","display_name":"Nan Zhang","orcid":"https://orcid.org/0000-0001-9620-5665"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"funder","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Zhang","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047846625","display_name":"Shiliang Sun","orcid":"https://orcid.org/0000-0001-7069-3752"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"funder","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiliang Sun","raw_affiliation_strings":["School of Computer Science and Technology, East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.205,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.800254,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"45","issue":"4","first_page":"4981","last_page":"4996"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9999,"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":0.9999,"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":0.9786,"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/T11309","display_name":"Music and Audio Processing","score":0.9532,"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"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.904303},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.61361986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.59675926},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.57742906},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.55335915},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.51559085},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4998951},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.45606294},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4501939},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30708075},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3198411","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35969573","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":["National Institutes of Health"],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"62006076"},{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"62076096"}],"datasets":[],"versions":[],"referenced_works_count":60,"referenced_works":["https://openalex.org/W1511494214","https://openalex.org/W1789163674","https://openalex.org/W1929219515","https://openalex.org/W1978371851","https://openalex.org/W1979089718","https://openalex.org/W1983193016","https://openalex.org/W1984674851","https://openalex.org/W2011208599","https://openalex.org/W2029438113","https://openalex.org/W2037537012","https://openalex.org/W2078559879","https://openalex.org/W2078932129","https://openalex.org/W2084616221","https://openalex.org/W2099302229","https://openalex.org/W2105482032","https://openalex.org/W2107018368","https://openalex.org/W2112056172","https://openalex.org/W2113108717","https://openalex.org/W2118382442","https://openalex.org/W2123502857","https://openalex.org/W2128061541","https://openalex.org/W2145812612","https://openalex.org/W2171701641","https://openalex.org/W2283896980","https://openalex.org/W2343110772","https://openalex.org/W2402972623","https://openalex.org/W2468738844","https://openalex.org/W2513005634","https://openalex.org/W2590019597","https://openalex.org/W2604269166","https://openalex.org/W2604723872","https://openalex.org/W2626473047","https://openalex.org/W2726969888","https://openalex.org/W2807737462","https://openalex.org/W2883939359","https://openalex.org/W2902925364","https://openalex.org/W2905279478","https://openalex.org/W2905361499","https://openalex.org/W2905470952","https://openalex.org/W2910861656","https://openalex.org/W2936565617","https://openalex.org/W2940583305","https://openalex.org/W2962919278","https://openalex.org/W2963102641","https://openalex.org/W2972477132","https://openalex.org/W2982595487","https://openalex.org/W2996095042","https://openalex.org/W2997546679","https://openalex.org/W3008974784","https://openalex.org/W3015356384","https://openalex.org/W3015435840","https://openalex.org/W3023566995","https://openalex.org/W3023780296","https://openalex.org/W3044063956","https://openalex.org/W3045832269","https://openalex.org/W3090103671","https://openalex.org/W3117734321","https://openalex.org/W3133439000","https://openalex.org/W4255601674","https://openalex.org/W4289360400"],"related_works":["https://openalex.org/W4318262572","https://openalex.org/W4250754046","https://openalex.org/W4243682621","https://openalex.org/W40745829","https://openalex.org/W2406638334","https://openalex.org/W2036849593","https://openalex.org/W2032728545","https://openalex.org/W1978357124","https://openalex.org/W1578824628","https://openalex.org/W1570805059"],"abstract_inverted_index":{"Multivariate":[0],"time":[1,12,28,55,143,213,219],"series":[2,13,29,56,144,214,220],"clustering":[3],"has":[4],"become":[5],"an":[6],"important":[7],"research":[8],"topic":[9],"in":[10],"the":[11,20,83,109,129,182,188],"learning":[14,68,75,102,106],"task,":[15,44],"which":[16,164],"aims":[17],"to":[18,49,121,152],"discover":[19,50],"correlation":[21],"among":[22,192],"multiple":[23],"sequences":[24],"and":[25,103,147,187],"partition":[26],"multivariate":[27,54,77,80,95,99,114,125,134,142,197,212,218],"data":[30],"into":[31],"several":[32],"subsets.":[33],"Although":[34],"there":[35],"are":[36,202],"currently":[37],"some":[38],"methods":[39],"that":[40,122,207],"can":[41,88,139],"handle":[42],"this":[43,59,178],"most":[45],"of":[46,85,111,116,123,136,171,184,199],"them":[47],"fail":[48],"informative":[51],"subsequences":[52,78],"from":[53,133,169],"instances.":[57],"In":[58,127,177],"paper,":[60],"we":[61,156],"first":[62],"propose":[63],"a":[64,93,158],"novel":[65,159],"unsupervised":[66],"shapelet":[67],"with":[69,113],"adaptive":[70],"neighbors":[71],"(USLA)":[72],"model":[73,163],"for":[74],"salient":[76],"(i.e.,":[79],"shapelets),":[81],"where":[82],"importance":[84,183],"each":[86,153,185],"variate":[87],"be":[89],"auto-determined":[90],"when":[91,195],"given":[92],"candidate":[94,196],"shapelet.":[96],"USLA":[97,112,161],"performs":[98],"shapelet-transformed":[100,130,166],"representation":[101],"local":[104],"structure":[105],"simultaneously,":[107],"but":[108],"performance":[110],"shapelets":[115,135,170,198],"different":[117,137,172,175,200],"lengths":[118,138,173,201],"is":[119],"comparable":[120],"isometric":[124],"shapelets.":[126],"fact,":[128],"representations":[131,167,194],"learned":[132,168],"all":[140],"represent":[141],"instances":[145],"separately":[146],"often":[148],"contain":[149],"complementary":[150],"information":[151],"other.":[154],"Therefore,":[155],"develop":[157],"multiview":[160,193],"(MUSLA)":[162],"treats":[165],"as":[174],"views.":[176],"way,":[179],"MUSLA":[180,208],"learns":[181],"view":[186],"neighbor":[189],"graph":[190],"matrix":[191],"determined.":[203],"Experimental":[204],"results":[205],"show":[206],"outperforms":[209],"other":[210],"state-of-the-art":[211],"algorithms":[215],"on":[216],"real-world":[217],"datasets.":[221]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4291910533","counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":9}],"updated_date":"2025-04-16T17:19:13.072357","created_date":"2022-08-16"}