{"id":"https://openalex.org/W2154553070","doi":"https://doi.org/10.1145/1401890.1401922","title":"Direct mining of discriminative and essential frequent patterns via model-based search tree","display_name":"Direct mining of discriminative and essential frequent patterns via model-based search tree","publication_year":2008,"publication_date":"2008-08-24","ids":{"openalex":"https://openalex.org/W2154553070","doi":"https://doi.org/10.1145/1401890.1401922","mag":"2154553070"},"language":"en","primary_location":{"id":"doi:10.1145/1401890.1401922","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401922","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5100380588","display_name":"Wei Fan","orcid":"https://orcid.org/0009-0008-1900-7081"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wei Fan","raw_affiliation_strings":["IBM T.J.Watson, Hawthorne, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM T.J.Watson, Hawthorne, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101884557","display_name":"Kun Zhang","orcid":"https://orcid.org/0000-0001-6397-9648"},"institutions":[{"id":"https://openalex.org/I169251466","display_name":"Xavier University of Louisiana","ror":"https://ror.org/0085d9t86","country_code":"US","type":"education","lineage":["https://openalex.org/I169251466"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kun Zhang","raw_affiliation_strings":["Xavier University of Louisiana, New Orleands, LA, USA"],"affiliations":[{"raw_affiliation_string":"Xavier University of Louisiana, New Orleands, LA, USA","institution_ids":["https://openalex.org/I169251466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101984697","display_name":"Hong Cheng","orcid":"https://orcid.org/0000-0002-4673-2587"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hong Cheng","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077201324","display_name":"Jing Gao","orcid":"https://orcid.org/0000-0003-1778-8909"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Gao","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047709762","display_name":"Xifeng Yan","orcid":"https://orcid.org/0009-0000-6508-4792"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xifeng Yan","raw_affiliation_strings":["IBM T.J.Watson, Hawthorne, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM T.J.Watson, Hawthorne, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019539533","display_name":"Jiawei Han","orcid":"https://orcid.org/0000-0002-3629-2696"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiawei Han","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036357902","display_name":"Philip S. Yu","orcid":"https://orcid.org/0000-0002-3491-5968"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip Yu","raw_affiliation_strings":["University of Illinois at Chi ago, Chicago, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Chi ago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111450401","display_name":"Olivier Verscheure","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Olivier Verscheure","raw_affiliation_strings":["IBM T.J.Watson, Hawthorne, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM T.J.Watson, Hawthorne, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100380588"],"corresponding_institution_ids":["https://openalex.org/I1341412227"],"apc_list":null,"apc_paid":null,"fwci":36.736,"has_fulltext":false,"cited_by_count":103,"citation_normalized_percentile":{"value":0.99704432,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"230","last_page":"238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9914000034332275,"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"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9492999911308289,"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/discriminative-model","display_name":"Discriminative model","score":0.9204140901565552},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7129876613616943},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.62535160779953},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5049758553504944},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5009284019470215},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49209994077682495},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4770595133304596},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4506816267967224},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.450074702501297},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43022918701171875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3414658308029175},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2098943591117859},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.18246760964393616}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9204140901565552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7129876613616943},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.62535160779953},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5049758553504944},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5009284019470215},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49209994077682495},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4770595133304596},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4506816267967224},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.450074702501297},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43022918701171875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3414658308029175},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2098943591117859},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.18246760964393616},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1401890.1401922","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1401890.1401922","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.215.7240","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.215.7240","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.uiuc.edu/homes/hanj/pdf/kdd08_weifan.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W159524162","https://openalex.org/W1484413656","https://openalex.org/W1506285740","https://openalex.org/W1584003909","https://openalex.org/W1623342295","https://openalex.org/W1985104873","https://openalex.org/W2039444222","https://openalex.org/W2064853889","https://openalex.org/W2101302040","https://openalex.org/W2108150886","https://openalex.org/W2108697621","https://openalex.org/W2108923196","https://openalex.org/W2111254498","https://openalex.org/W2115412287","https://openalex.org/W2116396873","https://openalex.org/W2117169652","https://openalex.org/W2135552269","https://openalex.org/W2136507529","https://openalex.org/W2136593687","https://openalex.org/W2147694185","https://openalex.org/W2154642793","https://openalex.org/W2155113671","https://openalex.org/W2158454296","https://openalex.org/W2161723275","https://openalex.org/W2164281374","https://openalex.org/W2167681385","https://openalex.org/W3144386677","https://openalex.org/W4252403066","https://openalex.org/W6628750762","https://openalex.org/W6675319365"],"related_works":["https://openalex.org/W2729514902","https://openalex.org/W2024160000","https://openalex.org/W2773500201","https://openalex.org/W2061273563","https://openalex.org/W2285052147","https://openalex.org/W4287995534","https://openalex.org/W2743258233","https://openalex.org/W2998168123","https://openalex.org/W1972656095","https://openalex.org/W2970216048"],"abstract_inverted_index":{"Frequent":[0],"patterns":[1,24,159,220,256],"provide":[2],"solutions":[3],"to":[4,66,89,133,157,229,243,249,252],"datasets":[5,265],"that":[6,116,165],"do":[7],"not":[8,167],"have":[9,56,248],"well-structured":[10],"feature":[11,51,180],"vectors.":[12],"However,":[13],"frequent":[14,34,47,195,236],"pattern":[15,35,132,237],"mining":[16,36],"is":[17,25,37,73,87,149,155,207],"non-trivial":[18],"since":[19],"the":[20,60,90,118,142,152,171,175,188,204,214,270],"number":[21,143],"of":[22,46,93,144,187,217],"unique":[23],"exponential":[26],"but":[27,203],"many":[28,54],"are":[29,182,266],"non-discriminative":[30],"and":[31,82,104,108,264],"correlated.":[32],"Currently,":[33],"performed":[38],"in":[39,59,77],"two":[40],"sequential":[41],"steps:":[42],"enumerating":[43],"a":[44,106,113,130],"set":[45],"patterns,":[48,234],"followed":[49],"by":[50,174,268],"selection.":[52],"Although":[53],"methods":[55],"been":[57],"proposed":[58,201],"past":[61],"few":[62],"years":[63],"on":[64,170,185],"how":[65],"perform":[67],"each":[68,125],"separate":[69],"step":[70],"efficiently,":[71],"there":[72],"still":[74],"limited":[75],"success":[76],"eventually":[78],"finding":[79],"highly":[80],"compact":[81],"discriminative":[83,131,219],"patterns.":[84],"The":[85,178],"culprit":[86],"due":[88,242],"inherent":[91],"nature":[92],"this":[94],"widely":[95],"adopted":[96],"two-step":[97,176],"approach.":[98],"This":[99],"paper":[100],"discusses":[101],"these":[102,231],"problems":[103,197],"proposes":[105],"new":[107,153],"different":[109,121],"method.":[110,177],"It":[111],"builds":[112],"decision":[114],"tree":[115],"partitions":[117],"data":[119],"onto":[120],"nodes.":[122],"Then":[123],"at":[124],"node,":[126],"it":[127],"directly":[128],"discovers":[129],"further":[134],"divide":[135],"its":[136],"examples":[137,145],"into":[138],"purer":[139],"subsets.":[140],"Since":[141],"towards":[146],"leaf":[147],"level":[148],"relatively":[150],"small,":[151],"approach":[154],"able":[156],"examine":[158],"with":[160],"extremely":[161,223],"low":[162,224,232],"global":[163],"support":[164,216,233],"could":[166],"be":[168,222,261],"enumerated":[169],"whole":[172],"dataset":[173],"discovered":[179],"vectors":[181],"more":[183,211,255],"accurate":[184],"some":[186,218],"most":[189,199],"difficult":[190],"graph":[191],"as":[192,194],"well":[193],"itemset":[196],"than":[198],"recently":[200],"algorithms":[202],"total":[205],"size":[206],"typically":[208],"50%":[209],"or":[210,247],"smaller.":[212],"Importantly,":[213],"minimum":[215],"can":[221,259],"(e.g.":[225],"0.03%).":[226],"In":[227],"order":[228],"enumerate":[230,250],"state-of-the-art":[235],"algorithm":[238],"either":[239],"cannot":[240],"finish":[241],"huge":[244],"memory":[245],"consumption":[246],"101":[251],"103":[253],"times":[254],"before":[257],"they":[258],"even":[260],"found.":[262],"Software":[263],"available":[267],"contacting":[269],"author.":[271]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":9},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":8}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
