{"id":"https://openalex.org/W2913723957","doi":"https://doi.org/10.1109/ssci.2018.8628933","title":"Identifying flight delay patterns using diverse subgroup discovery","display_name":"Identifying flight delay patterns using diverse subgroup discovery","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2913723957","doi":"https://doi.org/10.1109/ssci.2018.8628933","mag":"2913723957"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2018.8628933","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2018.8628933","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5068912127","display_name":"Hugo Manuel Proen\u00e7a","orcid":"https://orcid.org/0000-0001-7315-5925"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Hugo M. Proenca","raw_affiliation_strings":["LIACS, Leiden University, Leiden, Netherlands"],"affiliations":[{"raw_affiliation_string":"LIACS, Leiden University, Leiden, Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045916567","display_name":"Ruben Klijn","orcid":null},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ruben Klijn","raw_affiliation_strings":["LIACS, Leiden University, Leiden, Netherlands"],"affiliations":[{"raw_affiliation_string":"LIACS, Leiden University, Leiden, Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062646838","display_name":"Thomas B\u00e4ck","orcid":"https://orcid.org/0000-0001-6768-1478"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Thomas Back","raw_affiliation_strings":["LIACS, Leiden University, Leiden, Netherlands"],"affiliations":[{"raw_affiliation_string":"LIACS, Leiden University, Leiden, Netherlands","institution_ids":["https://openalex.org/I121797337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022646570","display_name":"Matthijs van Leeuwen","orcid":"https://orcid.org/0000-0002-0510-3549"},"institutions":[{"id":"https://openalex.org/I121797337","display_name":"Leiden University","ror":"https://ror.org/027bh9e22","country_code":"NL","type":"education","lineage":["https://openalex.org/I121797337"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Matthijs van Leeuwen","raw_affiliation_strings":["LIACS, Leiden University, Leiden, Netherlands"],"affiliations":[{"raw_affiliation_string":"LIACS, Leiden University, Leiden, Netherlands","institution_ids":["https://openalex.org/I121797337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5068912127"],"corresponding_institution_ids":["https://openalex.org/I121797337"],"apc_list":null,"apc_paid":null,"fwci":10.1111,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.97743277,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"60","last_page":"67"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11489","display_name":"Air Traffic Management and Optimization","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11489","display_name":"Air Traffic Management and Optimization","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9861999750137329,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12401","display_name":"Scheduling and Timetabling Solutions","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7363587617874146},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.5552331805229187},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5044888257980347},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.4633951783180237},{"id":"https://openalex.org/keywords/crew","display_name":"Crew","score":0.4628744423389435},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4238802194595337},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33112967014312744},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1151471734046936},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.0995345413684845}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7363587617874146},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5552331805229187},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5044888257980347},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.4633951783180237},{"id":"https://openalex.org/C2780179797","wikidata":"https://www.wikidata.org/wiki/Q345844","display_name":"Crew","level":2,"score":0.4628744423389435},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4238802194595337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33112967014312744},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1151471734046936},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0995345413684845},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C178802073","wikidata":"https://www.wikidata.org/wiki/Q8421","display_name":"Aeronautics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci.2018.8628933","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2018.8628933","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W135524293","https://openalex.org/W1970818056","https://openalex.org/W2010472418","https://openalex.org/W2015444745","https://openalex.org/W2029663307","https://openalex.org/W2072962100","https://openalex.org/W2077442669","https://openalex.org/W2078644189","https://openalex.org/W2089296357","https://openalex.org/W2104017387","https://openalex.org/W2108961154","https://openalex.org/W2113340410","https://openalex.org/W2129027474","https://openalex.org/W2132452100","https://openalex.org/W2153504810","https://openalex.org/W2161889111","https://openalex.org/W2238902321","https://openalex.org/W2338621248","https://openalex.org/W2538285964","https://openalex.org/W2564078846","https://openalex.org/W2586011290","https://openalex.org/W2604861992","https://openalex.org/W2950901155","https://openalex.org/W2998617017","https://openalex.org/W6684115925","https://openalex.org/W6891930171"],"related_works":["https://openalex.org/W1230495041","https://openalex.org/W2981238890","https://openalex.org/W3201287350","https://openalex.org/W3204452099","https://openalex.org/W2130466874","https://openalex.org/W258179848","https://openalex.org/W2395067109","https://openalex.org/W1784025083","https://openalex.org/W2885490362","https://openalex.org/W2368774684"],"abstract_inverted_index":{"Flight":[0],"delay":[1,96],"is":[2,115,250],"a":[3,16,110,139,170,195],"common":[4],"hassle":[5],"that":[6,69,143,151,200,205,215,226,240],"affects":[7],"around":[8],"one":[9],"fourth":[10],"of":[11,27,40,53,148,189],"flights":[12,109,190],"and":[13,43,81,164,186,238,258],"has":[14],"been":[15],"major":[17],"concern":[18],"for":[19,21,50,100,107,123],"airlines":[20,68],"decades.":[22],"Therefore,":[23],"an":[24,246],"increasing":[25],"amount":[26],"research":[28],"was":[29],"done":[30],"on":[31,77,174,192],"this":[32,130,201],"topic":[33],"in":[34,113,242],"recent":[35],"years.":[36],"Notably,":[37],"the":[38,51,62,149,155,175,213],"fields":[39],"machine":[41],"learning":[42],"data":[44,140,150,157,185],"mining":[45,141],"have":[46,194],"proposed":[47],"various":[48],"solutions":[49],"prediction":[52],"flight":[54,184],"delays,":[55],"typically":[56],"some":[57,161],"hours":[58],"before":[59],"departure.":[60,93],"However,":[61],"most":[63],"important":[64],"decisions":[65],"made":[66,85],"by":[67,169,210,260],"could":[70],"benefit":[71],"from":[72,154],"such":[73],"predictions,":[74],"i.e.,":[75],"those":[76],"scheduled":[78],"block":[79],"time":[80,112],"crew":[82],"schedules,":[83],"are":[84,98],"between":[86,218],"two":[87],"to":[88,92,121,126,133,145,160,182],"six":[89],"months":[90],"prior":[91],"Consequently,":[94],"late":[95],"predictions":[97],"useless":[99],"these":[101],"scheduling":[102,270],"tasks.As":[103],"accurately":[104],"predicting":[105],"delays":[106],"individual":[108],"long":[111],"advance":[114],"practically":[116],"infeasible,":[117,251],"we":[118,131],"instead":[119],"propose":[120,132],"search":[122],"circumstances":[124],"associated":[125],"large":[127,196],"delays.":[128],"For":[129],"use":[134],"diverse":[135,180,228],"Subgroup":[136],"Discovery":[137],"(SD),":[138],"technique":[142],"allows":[144],"discover":[146],"subsets":[147],"1)":[152],"deviate":[153],"overall":[156],"with":[158],"regard":[159],"target":[162],"variable,":[163],"2)":[165],"can":[166,206,221,254],"be":[167,207,222,255],"described":[168,259],"simple":[171],"conjunctive":[172],"query":[173],"other":[176],"variables.":[177],"We":[178,198,224],"apply":[179],"SD":[181,229,237],"historic":[183],"mine":[187],"subgroups":[188,204],"that,":[191],"average,":[193],"delay.":[197],"show":[199,225],"approach":[202],"gives":[203,230],"easily":[208],"understood":[209],"experts,":[211],"despite":[212],"fact":[214],"non-trivial":[216],"relations":[217],"multiple":[219],"variables":[220],"discovered.":[223],"using":[227],"less":[231],"redundant":[232],"results":[233],"than":[234],"standard":[235],"top-k":[236],"demonstrate":[239],"even":[241],"situations":[243],"where":[244],"inferring":[245],"accurate":[247],"predictive":[248],"model":[249],"local":[252,261],"deviations":[253],"effectively":[256],"captured":[257],"patterns,":[262],"potentially":[263],"providing":[264],"valuable":[265],"insights":[266],"for,":[267],"e.g.,":[268],"airline":[269],"problems.":[271]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
