{"id":"https://openalex.org/W1621075703","doi":"https://doi.org/10.1109/icacci.2015.7275753","title":"An adaptive method for mining frequent itemsets efficiently: An improved header tree method","display_name":"An adaptive method for mining frequent itemsets efficiently: An improved header tree method","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W1621075703","doi":"https://doi.org/10.1109/icacci.2015.7275753","mag":"1621075703"},"language":"en","primary_location":{"id":"doi:10.1109/icacci.2015.7275753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2015.7275753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5109883889","display_name":"O. Jamsheela","orcid":null},"institutions":[{"id":"https://openalex.org/I52703040","display_name":"Kannur University","ror":"https://ror.org/00zz2cd87","country_code":"IN","type":"education","lineage":["https://openalex.org/I52703040"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"O. Jamsheela","raw_affiliation_strings":["Department of Information Technology, Kannur University","Department of Information Technology, Kannur University, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Kannur University","institution_ids":["https://openalex.org/I52703040"]},{"raw_affiliation_string":"Department of Information Technology, Kannur University, India","institution_ids":["https://openalex.org/I52703040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055641056","display_name":"G. Raju","orcid":"https://orcid.org/0000-0002-7871-1801"},"institutions":[{"id":"https://openalex.org/I52703040","display_name":"Kannur University","ror":"https://ror.org/00zz2cd87","country_code":"IN","type":"education","lineage":["https://openalex.org/I52703040"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Raju G.","raw_affiliation_strings":["Department of Information Technology, Kannur University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Kannur University","institution_ids":["https://openalex.org/I52703040"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I52703040"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.05003173,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"179","issue":null,"first_page":"1078","last_page":"1084"},"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":0.9911999702453613,"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":0.9853000044822693,"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/header","display_name":"Header","score":0.8759739398956299},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.8340212106704712},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.8016160130500793},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7851340770721436},{"id":"https://openalex.org/keywords/apriori-algorithm","display_name":"Apriori algorithm","score":0.6718508005142212},{"id":"https://openalex.org/keywords/sorting","display_name":"Sorting","score":0.6362078785896301},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.5236634612083435},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5030013918876648},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5009980201721191},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.4853273928165436},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.43332168459892273},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4158678352832794},{"id":"https://openalex.org/keywords/trie","display_name":"Trie","score":0.4134528636932373},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.27128809690475464},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2249816656112671},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14442262053489685},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10925835371017456}],"concepts":[{"id":"https://openalex.org/C48105269","wikidata":"https://www.wikidata.org/wiki/Q1141160","display_name":"Header","level":2,"score":0.8759739398956299},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.8340212106704712},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.8016160130500793},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7851340770721436},{"id":"https://openalex.org/C81440476","wikidata":"https://www.wikidata.org/wiki/Q513511","display_name":"Apriori algorithm","level":3,"score":0.6718508005142212},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.6362078785896301},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.5236634612083435},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5030013918876648},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5009980201721191},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.4853273928165436},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.43332168459892273},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4158678352832794},{"id":"https://openalex.org/C190290938","wikidata":"https://www.wikidata.org/wiki/Q387015","display_name":"Trie","level":3,"score":0.4134528636932373},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.27128809690475464},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2249816656112671},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14442262053489685},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10925835371017456},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icacci.2015.7275753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icacci.2015.7275753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1484413656","https://openalex.org/W1970077009","https://openalex.org/W1984446671","https://openalex.org/W2011985274","https://openalex.org/W2014379975","https://openalex.org/W2024511949","https://openalex.org/W2034589438","https://openalex.org/W2036752235","https://openalex.org/W2043422173","https://openalex.org/W2061553791","https://openalex.org/W2071323385","https://openalex.org/W2108873424","https://openalex.org/W2110893883","https://openalex.org/W2143428105","https://openalex.org/W2166559705","https://openalex.org/W4252403066","https://openalex.org/W6628750762"],"related_works":["https://openalex.org/W2390051172","https://openalex.org/W2297208791","https://openalex.org/W2367209111","https://openalex.org/W2351000793","https://openalex.org/W2366790077","https://openalex.org/W2348276166","https://openalex.org/W3034345083","https://openalex.org/W2607264580","https://openalex.org/W3012205960","https://openalex.org/W1483188779"],"abstract_inverted_index":{"Data":[0],"mining":[1,26,38,130,163,179],"has":[2,8,31],"become":[3],"an":[4,124,159],"important":[5,46,125],"field":[6],"and":[7,39,101,158],"been":[9,32],"applied":[10],"extensively":[11],"across":[12],"many":[13,88,102],"different":[14],"areas.":[15],"Mining":[16],"frequent":[17,36,171,185],"itemsets":[18],"from":[19],"a":[20,113,151],"transaction":[21],"database":[22],"is":[23,41,116,123,137,182],"crucial":[24],"for":[25,35,184],"association":[27,52],"rules.":[28],"FP-growth":[29,71,84],"algorithm":[30,47],"widely":[33],"used":[34,117],"pattern":[37,96,172,186],"it":[40,55],"one":[42],"of":[43,76,82,95,106,142,169],"the":[44,59,63,74,83,90,107,129,140,143,167,170,178],"most":[45],"proposed":[48],"to":[49,62,165],"efficiently":[50],"mine":[51],"rules":[53],"because":[54],"can":[56],"dramatically":[57],"improve":[58,166],"performance":[60,81,168],"compared":[61],"Apriori":[64],"algorithm.":[65],"Many":[66],"investigations":[67],"have":[68],"proved":[69],"that":[70,177],"method":[72,75,85],"outperforms":[73],"Apriori-like":[77],"candidate":[78],"generation.":[79],"The":[80,132],"depends":[86],"on":[87],"factors;":[89],"data":[91,126],"structures,":[92],"recursive":[93],"creation":[94],"trees,":[97],"searching,":[98],"sorting,":[99],"insertion":[100],"more.":[103],"In":[104,146],"all":[105],"algorithms":[108],"which":[109],"are":[110],"using":[111],"fp-tree,":[112],"header":[114,144],"table":[115,122],"with":[118,139,180],"sorted":[119],"items.":[120],"Header":[121,155,161],"structure":[127],"in":[128],"process.":[131],"main":[133],"datastructure":[134],"(frequent":[135],"trees)":[136],"created":[138],"use":[141],"table.":[145],"this":[147],"paper":[148],"we":[149],"suggest":[150],"new":[152],"Binary":[153],"Search":[154],"Three":[156],"(BSHT)":[157],"Improved":[160],"Tree":[162],"(IHT-growth)":[164],"mining.":[173,187],"Experimental":[174],"results":[175],"show":[176],"BSHT":[181],"efficient":[183]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
