{"id":"https://openalex.org/W1910036675","doi":"https://doi.org/10.3233/ida-150754","title":"A lazy associative classifier for time series","display_name":"A lazy associative classifier for time series","publication_year":2015,"publication_date":"2015-09-08","ids":{"openalex":"https://openalex.org/W1910036675","doi":"https://doi.org/10.3233/ida-150754","mag":"1910036675"},"language":"en","primary_location":{"id":"doi:10.3233/ida-150754","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-150754","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-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/A5050517295","display_name":"Jidong Yuan","orcid":"https://orcid.org/0000-0003-2654-3372"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jidong Yuan","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100698059","display_name":"Zhihai Wang","orcid":"https://orcid.org/0000-0002-9971-9167"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhihai Wang","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087942198","display_name":"Meng Han","orcid":"https://orcid.org/0000-0002-1333-1090"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Han","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100376523","display_name":"Yange Sun","orcid":"https://orcid.org/0000-0003-3305-4622"},"institutions":[{"id":"https://openalex.org/I130750295","display_name":"Xinyang Normal University","ror":"https://ror.org/0190x2a66","country_code":"CN","type":"education","lineage":["https://openalex.org/I130750295"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yange Sun","raw_affiliation_strings":["Xinyang Normal University, Xinyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinyang Normal University, Xinyang, China","institution_ids":["https://openalex.org/I130750295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100698059"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":{"value":4150,"currency":"USD","value_usd":4150},"apc_paid":null,"fwci":1.8691,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.85521983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"19","issue":"5","first_page":"983","last_page":"1002"},"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":0.967199981212616,"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":0.9648000001907349,"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/associative-property","display_name":"Associative property","score":0.7419866919517517},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.528915286064148},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4811938405036926},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4601711630821228},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.44688504934310913},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3749851584434509},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.290350079536438},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.0388699471950531}],"concepts":[{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.7419866919517517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.528915286064148},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4811938405036926},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4601711630821228},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.44688504934310913},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3749851584434509},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.290350079536438},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0388699471950531},{"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":1,"locations":[{"id":"doi:10.3233/ida-150754","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-150754","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W151863654","https://openalex.org/W163580248","https://openalex.org/W1491931038","https://openalex.org/W1535228720","https://openalex.org/W1623342295","https://openalex.org/W1778796894","https://openalex.org/W1853995153","https://openalex.org/W2011208599","https://openalex.org/W2029438113","https://openalex.org/W2098759488","https://openalex.org/W2108923196","https://openalex.org/W2111254498","https://openalex.org/W2116396873","https://openalex.org/W2117169652","https://openalex.org/W2118938540","https://openalex.org/W2123502857","https://openalex.org/W2125055259","https://openalex.org/W2149706766","https://openalex.org/W2154642793","https://openalex.org/W2164274563","https://openalex.org/W2167681385","https://openalex.org/W2170726034","https://openalex.org/W2399056987"],"related_works":["https://openalex.org/W1492794944","https://openalex.org/W1919101720","https://openalex.org/W169603398","https://openalex.org/W2913274341","https://openalex.org/W2049033869","https://openalex.org/W4390822878","https://openalex.org/W2033914206","https://openalex.org/W2148338580","https://openalex.org/W2042327336","https://openalex.org/W2049864679"],"abstract_inverted_index":{"Association":[0],"rule":[1,18],"mining":[2,52],"that":[3,36,67,166,189],"mainly":[4],"focuses":[5],"on":[6,128],"symbolic":[7,62],"items":[8],"presented":[9],"in":[10,40,54,88,122,135],"transactions":[11],"has":[12],"attracted":[13],"considerable":[14],"interest":[15],"since":[16],"a":[17,20,28,32,61,84,94,116,129],"provides":[19],"concise":[21],"and":[22,72,79,106,177],"intuitive":[23],"description":[24],"of":[25,34,47,57,147],"knowledge.":[26],"However,":[27],"time":[29,58,74,172],"series":[30,59,75,173],"is":[31,37,86,98,126,134,150],"sequence":[33],"data":[35,76,157],"typically":[38],"recorded":[39],"temporal":[41],"order":[42,50],"at":[43,111],"fixed":[44],"inte":[45],"rvals":[46],"time.":[48],"In":[49,184],"to":[51,83,100,137,153],"rules":[53],"the":[55,70,108,124,159,180,191],"context":[56],"data,":[60],"aggregate":[63],"approximation":[64],"(SAX)":[65],"representation":[66],"could":[68],"discretize":[69],"real-valued":[71],"high-dimensional":[73],"into":[77],"segments":[78],"convert":[80],"each":[81],"segment":[82],"symbol":[85],"applied":[87],"this":[89,92],"paper.":[90],"On":[91],"basis,":[93],"modified":[95],"CBA":[96,142],"algorithm":[97],"proposed":[99,195],"discover":[101],"Class":[102],"Sequential":[103],"Rules":[104],"(CSRs)":[105],"make":[107],"final":[109],"prediction":[110],"first.":[112],"Then":[113],"we":[114],"propose":[115],"new":[117],"lazy":[118,168],"associative":[119,169,199],"classification":[120,139,170],"method,":[121],"which":[123,143],"computation":[125],"performed":[127],"demand":[130],"driven":[131],"basis.":[132],"This":[133],"contrast":[136],"rule-based":[138],"methods":[140,188],"like":[141],"generate":[144],"excessive":[145],"number":[146],"rules,":[148],"but":[149],"still":[151],"unable":[152],"cover":[154],"some":[155],"test":[156],"with":[158,179],"discovered":[160],"rules.":[161],"Various":[162],"experimental":[163],"results":[164],"show":[165],"our":[167],"for":[171,196],"can":[174],"be":[175],"interpretable":[176],"competitive":[178],"current":[181],"state-of-the-art":[182],"algorithm.":[183],"addition,":[185],"four":[186],"different":[187],"select":[190],"mined":[192],"CSR(s)":[193],"are":[194],"carrying":[197],"out":[198],"classification.":[200]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
