{"id":"https://openalex.org/W2296225073","doi":"https://doi.org/10.1109/bigcomp.2016.7425807","title":"Top-k high utility itemset mining based on utility-list structures","display_name":"Top-k high utility itemset mining based on utility-list structures","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2296225073","doi":"https://doi.org/10.1109/bigcomp.2016.7425807","mag":"2296225073"},"language":"en","primary_location":{"id":"doi:10.1109/bigcomp.2016.7425807","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigcomp.2016.7425807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Big Data and Smart Computing (BigComp)","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/A5050575161","display_name":"Serin Lee","orcid":"https://orcid.org/0000-0002-7051-7828"},"institutions":[{"id":"https://openalex.org/I165677929","display_name":"Sungshin Women's University","ror":"https://ror.org/0500xzf72","country_code":"KR","type":"education","lineage":["https://openalex.org/I165677929"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Serin Lee","raw_affiliation_strings":["Department of Computer Science, Sungshin Women's University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Sungshin Women's University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I165677929"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103338647","display_name":"Jong Soo Park","orcid":null},"institutions":[{"id":"https://openalex.org/I165677929","display_name":"Sungshin Women's University","ror":"https://ror.org/0500xzf72","country_code":"KR","type":"education","lineage":["https://openalex.org/I165677929"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jong Soo Park","raw_affiliation_strings":["School of Information Technology, Sungshin Women's University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, Sungshin Women's University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I165677929"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5050575161"],"corresponding_institution_ids":["https://openalex.org/I165677929"],"apc_list":null,"apc_paid":null,"fwci":3.3175,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.92862584,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"101","last_page":"108"},"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":0.9902999997138977,"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/T11106","display_name":"Data Management and Algorithms","score":0.9786999821662903,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6293684840202332},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41984400153160095}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6293684840202332},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41984400153160095}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigcomp.2016.7425807","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigcomp.2016.7425807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Big Data and Smart Computing (BigComp)","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":22,"referenced_works":["https://openalex.org/W1484413656","https://openalex.org/W1570170793","https://openalex.org/W1966426226","https://openalex.org/W1986572138","https://openalex.org/W2017264939","https://openalex.org/W2049785083","https://openalex.org/W2057858164","https://openalex.org/W2064853889","https://openalex.org/W2075138046","https://openalex.org/W2094770144","https://openalex.org/W2099398039","https://openalex.org/W2112307926","https://openalex.org/W2125352627","https://openalex.org/W2151028259","https://openalex.org/W2161637667","https://openalex.org/W2162214498","https://openalex.org/W2346466150","https://openalex.org/W4252403066","https://openalex.org/W4253376897","https://openalex.org/W6628750762","https://openalex.org/W6634075485","https://openalex.org/W6676703616"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"Top-k":[0],"high":[1,30,76],"utility":[2,20,31,77,118],"itemset":[3,32],"mining":[4,33,98],"refers":[5],"to":[6,73,113,132],"the":[7,19,38,43,49,94,99,115,144],"discovery":[8],"of":[9,21,51],"top-k":[10,29,75],"patterns":[11,44],"using":[12,107],"a":[13,24,69,82,105],"user-specified":[14],"value":[15],"k":[16],"by":[17],"considering":[18],"items":[22],"in":[23,45,151],"transactional":[25],"database.":[26],"Since":[27],"existing":[28],"algorithms":[34,149],"are":[35,62,130],"based":[36],"on":[37,93,139],"pattern-growth":[39],"method,":[40],"they":[41],"search":[42,95,108],"two":[46,122],"steps.":[47],"Therefore,":[48],"generation":[50],"many":[52],"candidates":[53],"and":[54,153],"additional":[55,123],"database":[56],"scan":[57],"for":[58,97,110,125],"calculating":[59,126],"exact":[60],"utilities":[61,129],"unavoidable.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67],"propose":[68],"new":[70,83],"algorithm,":[71],"TKUL-Miner,":[72],"mine":[74],"itemsets":[78,135],"efficiently.":[79],"It":[80],"utilizes":[81],"utility-list":[84],"structure":[85],"which":[86],"stores":[87],"necessary":[88],"information":[89],"at":[90],"each":[91],"node":[92],"tree":[96],"itemsets.":[100],"The":[101],"proposed":[102],"algorithm":[103],"has":[104],"strategy":[106],"order":[109],"specific":[111],"region":[112],"raise":[114],"border":[116],"minimum":[117],"threshold":[119],"rapidly.":[120],"Moreover,":[121],"strategies":[124],"smaller":[127],"overestimated":[128],"suggested":[131],"prune":[133],"unpromising":[134],"effectively.":[136],"Experimental":[137],"results":[138],"various":[140],"datasets":[141],"showed":[142],"that":[143],"TKUL-Miner":[145],"outperforms":[146],"other":[147],"recent":[148],"both":[150],"runtime":[152],"memory":[154],"efficiency.":[155]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}