{"id":"https://openalex.org/W2902143415","doi":"https://doi.org/10.1145/3289430.3289445","title":"A Spark-based Incremental Algorithm for Frequent Itemset Mining","display_name":"A Spark-based Incremental Algorithm for Frequent Itemset Mining","publication_year":2018,"publication_date":"2018-10-24","ids":{"openalex":"https://openalex.org/W2902143415","doi":"https://doi.org/10.1145/3289430.3289445","mag":"2902143415"},"language":"en","primary_location":{"id":"doi:10.1145/3289430.3289445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3289430.3289445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things","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/A5024412401","display_name":"Haoxing Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haoxing Wen","raw_affiliation_strings":["Nankai University, Tianjin China"],"affiliations":[{"raw_affiliation_string":"Nankai University, Tianjin China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071230786","display_name":"Mingdong Kou","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingdong Kou","raw_affiliation_strings":["Nankai University, Tianjin China"],"affiliations":[{"raw_affiliation_string":"Nankai University, Tianjin China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110585591","display_name":"Hengyi He","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengyi He","raw_affiliation_strings":["Nankai University, Tianjin China"],"affiliations":[{"raw_affiliation_string":"Nankai University, Tianjin China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100373866","display_name":"Xiaoguang Li","orcid":"https://orcid.org/0000-0002-7307-6263"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoguang Li","raw_affiliation_strings":["Nankai University, Tianjin China"],"affiliations":[{"raw_affiliation_string":"Nankai University, Tianjin China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083156453","display_name":"Huaixiao Tou","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huaixiao Tou","raw_affiliation_strings":["Fudan University, Shanghai China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100952780","display_name":"Yulu Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yulu Yang","raw_affiliation_strings":["Nankai University, Tianjin China"],"affiliations":[{"raw_affiliation_string":"Nankai University, Tianjin China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5024412401"],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":1.1781,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.85916682,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"53","last_page":"58"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":1.0,"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":1.0,"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":0.9793000221252441,"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":0.9761999845504761,"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/apriori-algorithm","display_name":"Apriori algorithm","score":0.8291342258453369},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.8082712292671204},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7856598496437073},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.7423257827758789},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.6721396446228027},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5433034300804138},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3452731966972351}],"concepts":[{"id":"https://openalex.org/C81440476","wikidata":"https://www.wikidata.org/wiki/Q513511","display_name":"Apriori algorithm","level":3,"score":0.8291342258453369},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.8082712292671204},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7856598496437073},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7423257827758789},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.6721396446228027},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5433034300804138},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3452731966972351},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3289430.3289445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3289430.3289445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1484413656","https://openalex.org/W1524454721","https://openalex.org/W1992540139","https://openalex.org/W2095535748","https://openalex.org/W2114624736","https://openalex.org/W2131975293","https://openalex.org/W2166559705","https://openalex.org/W2998574808"],"related_works":["https://openalex.org/W2390051172","https://openalex.org/W128746893","https://openalex.org/W2390733913","https://openalex.org/W2373679199","https://openalex.org/W2380019578","https://openalex.org/W2204362737","https://openalex.org/W2375024673","https://openalex.org/W4238174620","https://openalex.org/W2949943516","https://openalex.org/W2366239766"],"abstract_inverted_index":{"Association":[0],"rule":[1,40],"mining":[2,154],"plays":[3],"an":[4,21,59,88],"important":[5],"role":[6],"in":[7,38,47,93,110],"many":[8,48],"areas,":[9],"including":[10],"market":[11],"basket":[12],"analysis,":[13],"intrusion":[14],"detection,":[15],"bioinformatics":[16],"and":[17,82,127,160],"so":[18],"on.":[19],"As":[20,96],"efficient":[22],"approach":[23],"of":[24,54,113,133,153],"finding":[25],"frequent":[26,76,104,108,155],"itemset":[27,77,105,109,156],"among":[28],"large":[29,81,159],"datasets,":[30],"several":[31],"parallel":[32,70,90],"Apriori-based":[33,71,91],"algorithms":[34,72],"are":[35,44],"widely":[36],"used":[37],"association":[39],"mining.":[41],"Moreover,":[42],"datasets":[43,53,98,117],"always":[45],"changed":[46],"real-world":[49,137],"applications.":[50],"For":[51],"example,":[52],"the":[55,65,68,75,97,103,115,122,143,147,151,158],"purchased":[56],"products":[57],"from":[58,118],"e-commercial":[60],"website":[61],"is":[62,140],"growing":[63],"all":[64],"time.":[66],"However,":[67],"existing":[69],"cannot":[73],"update":[74],"efficiently":[78],"for":[79],"these":[80],"evolving":[83,161],"datasets.":[84,138],"So":[85],"we":[86],"propose":[87],"incremental":[89],"algorithm":[92,101,124,149],"this":[94],"paper.":[95],"increase,":[99],"our":[100],"updates":[102],"based":[106],"on":[107,125,135,157],"previous,":[111],"instead":[112],"re-computing":[114],"whole":[116],"scratch.":[119],"We":[120],"implement":[121],"proposed":[123,148],"Spark":[126],"evaluate":[128],"its":[129],"performance":[130,152],"via":[131],"groups":[132],"experiments":[134],"some":[136],"It":[139],"demonstrated":[141],"by":[142],"experimental":[144],"results":[145],"that":[146],"improves":[150],"data":[162],"sets":[163],"significantly.":[164]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}