{"id":"https://openalex.org/W2945553664","doi":"https://doi.org/10.1109/rivf.2019.8713620","title":"An Effective Implementation of Motif-Based Time Series Classification","display_name":"An Effective Implementation of Motif-Based Time Series Classification","publication_year":2019,"publication_date":"2019-03-01","ids":{"openalex":"https://openalex.org/W2945553664","doi":"https://doi.org/10.1109/rivf.2019.8713620","mag":"2945553664"},"language":"en","primary_location":{"id":"doi:10.1109/rivf.2019.8713620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rivf.2019.8713620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF)","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/A5112436766","display_name":"Nguyen Van Kier","orcid":null},"institutions":[{"id":"https://openalex.org/I47265099","display_name":"Ho Chi Minh City University of Technology","ror":"https://ror.org/04qva2324","country_code":"VN","type":"education","lineage":["https://openalex.org/I123565023","https://openalex.org/I47265099"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Nguyen Van Kier","raw_affiliation_strings":["Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam","institution_ids":["https://openalex.org/I47265099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059729965","display_name":"Duong Tuan Anh","orcid":"https://orcid.org/0000-0003-3340-7117"},"institutions":[{"id":"https://openalex.org/I47265099","display_name":"Ho Chi Minh City University of Technology","ror":"https://ror.org/04qva2324","country_code":"VN","type":"education","lineage":["https://openalex.org/I123565023","https://openalex.org/I47265099"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Duong Tuan Anh","raw_affiliation_strings":["Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam","institution_ids":["https://openalex.org/I47265099"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I47265099"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.03773815,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9431999921798706,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9394000172615051,"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/computer-science","display_name":"Computer science","score":0.7306473255157471},{"id":"https://openalex.org/keywords/motif","display_name":"Motif (music)","score":0.5773220062255859},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5693607330322266},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5602768659591675},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5137046575546265},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5079336762428284},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4732397496700287},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4674200713634491},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46145638823509216},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.46131300926208496},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4585520923137665},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4507640600204468},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32756519317626953},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3201824426651001}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7306473255157471},{"id":"https://openalex.org/C32276052","wikidata":"https://www.wikidata.org/wiki/Q908349","display_name":"Motif (music)","level":2,"score":0.5773220062255859},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5693607330322266},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5602768659591675},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5137046575546265},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5079336762428284},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4732397496700287},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4674200713634491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46145638823509216},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.46131300926208496},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4585520923137665},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4507640600204468},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32756519317626953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3201824426651001},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rivf.2019.8713620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rivf.2019.8713620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF)","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":14,"referenced_works":["https://openalex.org/W1513731586","https://openalex.org/W1562135275","https://openalex.org/W1595324333","https://openalex.org/W1662642097","https://openalex.org/W1903304966","https://openalex.org/W2006761268","https://openalex.org/W2029912643","https://openalex.org/W2034101327","https://openalex.org/W2066796814","https://openalex.org/W2098759488","https://openalex.org/W2247203673","https://openalex.org/W4242702158","https://openalex.org/W6635575044","https://openalex.org/W6637283803"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W1968695676"],"abstract_inverted_index":{"Time":[0],"series":[1,11,54,74,83,98,150,170,188],"classification":[2,24,75,94,171],"is":[3,47],"one":[4],"of":[5,19,92,108,148,154,175],"the":[6,16,43,90,109,116,173],"crucial":[7],"tasks":[8],"in":[9,26,143],"time":[10,20,34,53,73,82,97,149,169,187],"data":[12,27,55],"mining.":[13],"Due":[14],"to":[15,50,80,84,151],"unique":[17],"characteristics":[18],"series,":[21],"most":[22],"classic":[23],"algorithms":[25,95],"mining":[28],"do":[29],"not":[30,176],"work":[31],"well":[32],"for":[33,72,96,167,186],"series.":[35],"So":[36],"far,":[37],"empirical":[38],"evidence":[39],"has":[40],"shown":[41],"that":[42,68,141,162],"nearest":[44],"neighbor":[45,179],"method":[46,113,127,140,166],"very":[48],"effective":[49,106],"classify":[51],"directly":[52],"without":[56],"any":[57],"feature":[58,85],"extraction":[59],"step.":[60],"Recently,":[61],"there":[62],"have":[63],"been":[64],"two":[65],"research":[66],"works":[67],"proposed":[69,164],"a":[70,123,138,146,152],"framework":[71,88],"which":[76,120],"uses":[77],"motif":[78,117,125,132],"information":[79],"convert":[81],"vectors.":[86],"This":[87],"facilitates":[89],"use":[91],"classical":[93],"classification.":[99,189],"In":[100,111],"this":[101],"paper,":[102],"we":[103,114,136],"present":[104],"an":[105,130],"implementation":[107,165],"framework.":[110],"our":[112,163],"exploit":[115],"discovery":[118,126,133],"algorithm":[119,180],"combines":[121],"EP-C,":[122],"segmentation-based":[124,139],"and":[128,184],"MK,":[129],"exact":[131],"method.":[134],"Furthermore,":[135],"devise":[137],"helps":[142],"transforming":[144],"efficiently":[145],"set":[147,153],"feature-vectors.":[155],"Experimental":[156],"results":[157],"on":[158],"benchmark":[159],"datasets":[160],"reveal":[161],"motif-based":[168],"improves":[172],"accuracy":[174],"only":[177],"1-nearest":[178],"but":[181],"also":[182],"SVMs":[183],"ANNs":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}