{"id":"https://openalex.org/W4401352032","doi":"https://doi.org/10.14778/3675034.3675055","title":"Efficient Discovery of Significant Patterns with Few-Shot Resampling","display_name":"Efficient Discovery of Significant Patterns with Few-Shot Resampling","publication_year":2024,"publication_date":"2024-06-01","ids":{"openalex":"https://openalex.org/W4401352032","doi":"https://doi.org/10.14778/3675034.3675055"},"language":"en","primary_location":{"id":"doi:10.14778/3675034.3675055","is_oa":false,"landing_page_url":"http://dx.doi.org/10.14778/3675034.3675055","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/11577/3524906","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021603227","display_name":"Leonardo Pellegrina","orcid":"https://orcid.org/0000-0002-6601-5526"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Leonardo Pellegrina","raw_affiliation_strings":["Dept. of Information Engineering, University of Padova, Padova, Italy"],"affiliations":[{"raw_affiliation_string":"Dept. of Information Engineering, University of Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060279781","display_name":"Fabio Vandin","orcid":"https://orcid.org/0000-0003-2244-2320"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fabio Vandin","raw_affiliation_strings":["Dept. of Information Engineering, University of Padova, Padova, Italy"],"affiliations":[{"raw_affiliation_string":"Dept. of Information Engineering, University of Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021603227"],"corresponding_institution_ids":["https://openalex.org/I138689650"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18694002,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"10","first_page":"2668","last_page":"2680"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11269","display_name":"Algorithms and Data Compression","score":0.9976000189781189,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/shot","display_name":"Shot (pellet)","score":0.6238594651222229},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5731192231178284},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.516646683216095},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48843780159950256},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4072364866733551},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3485877513885498},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3402289152145386},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08340871334075928},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.06863486766815186}],"concepts":[{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6238594651222229},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5731192231178284},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.516646683216095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48843780159950256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4072364866733551},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3485877513885498},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3402289152145386},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08340871334075928},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.06863486766815186},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/3675034.3675055","is_oa":false,"landing_page_url":"http://dx.doi.org/10.14778/3675034.3675055","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:www.research.unipd.it:11577/3524906","is_oa":true,"landing_page_url":"https://hdl.handle.net/11577/3524906","pdf_url":null,"source":{"id":"https://openalex.org/S4306402547","display_name":"Padua Research Archive (University of Padova)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:www.research.unipd.it:11577/3524906","is_oa":true,"landing_page_url":"https://hdl.handle.net/11577/3524906","pdf_url":null,"source":{"id":"https://openalex.org/S4306402547","display_name":"Padua Research Archive (University of Padova)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138689650","host_organization_name":"University of Padua","host_organization_lineage":["https://openalex.org/I138689650"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W568673721","https://openalex.org/W607505555","https://openalex.org/W1989274820","https://openalex.org/W1991417304","https://openalex.org/W2014286755","https://openalex.org/W2027670229","https://openalex.org/W2030764200","https://openalex.org/W2040820996","https://openalex.org/W2059175149","https://openalex.org/W2059245204","https://openalex.org/W2070161269","https://openalex.org/W2104579579","https://openalex.org/W2110065044","https://openalex.org/W2132452100","https://openalex.org/W2143211346","https://openalex.org/W2164960226","https://openalex.org/W2166559705","https://openalex.org/W2338621248","https://openalex.org/W2754504587","https://openalex.org/W2796349379","https://openalex.org/W2909716785","https://openalex.org/W2950421339","https://openalex.org/W2954523198","https://openalex.org/W2965505261","https://openalex.org/W3025180762","https://openalex.org/W3034606263","https://openalex.org/W3092321452","https://openalex.org/W3103529187","https://openalex.org/W4285337927","https://openalex.org/W4317641498","https://openalex.org/W4385565193"],"related_works":["https://openalex.org/W2497720472","https://openalex.org/W4292659306","https://openalex.org/W3044321615","https://openalex.org/W4294892107","https://openalex.org/W2806221744","https://openalex.org/W2326937258","https://openalex.org/W394267150","https://openalex.org/W2773965352","https://openalex.org/W2357748469","https://openalex.org/W2392917037"],"abstract_inverted_index":{"Significant":[0],"pattern":[1,80,102],"mining":[2,8,162],"is":[3,40,49,84,259],"a":[4,21,65,79,119,157,182,203,225,233,241,267],"fundamental":[5],"task":[6],"in":[7,236,261],"transactional":[9],"data,":[10],"requiring":[11,266],"to":[12,41,53,74,85,112,140,193,196,223],"identify":[13,141],"patterns":[14,43,126,144,164],"significantly":[15],"associated":[16],"with":[17,46,51,81,145],"the":[18,24,38,47,61,76,82,95,101,104,149,169,199,207,218],"value":[19],"of":[20,59,67,78,98,151,168,185,202,209,228,270],"given":[22],"feature,":[23],"target.":[25,105],"In":[26],"several":[27,107,253],"applications,":[28],"such":[29,127],"as":[30,128],"biomedicine,":[31],"basket":[32],"market":[33],"analysis,":[34],"and":[35,103,123,178,249],"social":[36],"networks,":[37],"goal":[39],"discover":[42],"whose":[44],"association":[45,77],"target":[48,83],"defined":[50],"respect":[52],"an":[54,137],"underlying":[55],"population,":[56],"or":[57,69],"process,":[58],"which":[60],"dataset":[62],"represents":[63],"only":[64],"collection":[66],"observations,":[68],"samples.":[70],"A":[71],"natural":[72],"way":[73],"capture":[75],"consider":[86,245],"its":[87,92],"statistical":[88],"significance":[89,208],",":[90],"assessing":[91],"deviation":[93,201,220],"from":[94],"(null)":[96],"hypothesis":[97],"independence":[99],"between":[100],"While":[106],"algorithms":[108],"have":[109],"been":[110],"proposed":[111],"find":[113],"statistically":[114,142],"significant":[115,143,163,238,246,263],"patterns,":[116,173,177],"it":[117],"remains":[118],"computationally":[120],"demanding":[121],"task,":[122],"for":[124,161],"complex":[125],"subgroups,":[129,264],"no":[130],"efficient":[131,138],"solution":[132],"exists.":[133],"We":[134],"present":[135],"FSR,":[136],"algorithm":[139],"rigorous":[146],"guarantees":[147],"on":[148,156,213,217,252],"probability":[150],"false":[152],"discoveries.":[153],"FSR":[154,180,211,258],"builds":[155,212],"novel":[158,214],"general":[159],"framework":[160],"that":[165,221,257],"captures":[166],"some":[167],"most":[170],"commonly":[171],"considered":[172],"including":[174],"itemsets,":[175],"sequential":[176],"subgroups.":[179],"uses":[181],"small":[183,226,268],"number":[184,227,269],"resampled":[186,229,271],"datasets,":[187,230],"obtained":[188],"by":[189],"assigning":[190],"i.i.d.":[191],"labels":[192],"each":[194],"transaction,":[195],"rigorously":[197],"bound":[198],"supremum":[200,219],"quality":[204],"statistic":[205],"measuring":[206],"patterns.":[210,239],"tight":[215],"bounds":[216],"require":[222],"mine":[224],"while":[231,265],"providing":[232],"high":[234],"effectiveness":[235],"discovering":[237,262],"As":[240],"test":[242],"case,":[243],"we":[244],"subgroup":[247],"mining,":[248],"our":[250],"evaluation":[251],"real":[254],"datasets":[255],"shows":[256],"effective":[260],"datasets.":[272]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}