{"id":"https://openalex.org/W2790080700","doi":null,"title":"A Guided FP-growth algorithm for fast mining of frequent itemsets from big data.","display_name":"A Guided FP-growth algorithm for fast mining of frequent itemsets from big data.","publication_year":2018,"publication_date":"2018-03-18","ids":{"openalex":"https://openalex.org/W2790080700","mag":"2790080700"},"language":"en","primary_location":{"id":"mag:2790080700","doi":null,"is_oa":false,"landing_page_url":"http://dblp.uni-trier.de/db/journals/corr/corr1803.html#abs-1803-06632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5081656431","display_name":"Lior Shabtay","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lior Shabtay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068048608","display_name":"Rami Yaari","orcid":"https://orcid.org/0000-0002-8808-8937","observed_orcids":["https://orcid.org/0000-0002-8808-8937"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rami Yaari","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8808-8937","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5021730055","display_name":"Itai Dattner","orcid":"https://orcid.org/0000-0001-5316-8964","observed_orcids":["https://orcid.org/0000-0001-5316-8964"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Itai Dattner","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5316-8964","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.279,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67141101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.0,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fp-growth-algorithm","display_name":"FP-Growth algorithm","score":0.7839999794960022},{"id":"https://openalex.org/keywords/frequent-itemset-mining","display_name":"frequent itemset mining","score":0.9879999756813049},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.9750000238418579},{"id":"https://openalex.org/keywords/big-data-mining","display_name":"big data mining","score":0.6290000081062317},{"id":"https://openalex.org/keywords/rule-generation","display_name":"rule generation","score":0.48399999737739563}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7849069833755493},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.644797146320343},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5849212408065796},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5296012759208679},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5144360661506653},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27043503522872925}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2790080700","doi":null,"is_oa":false,"landing_page_url":"http://dblp.uni-trier.de/db/journals/corr/corr1803.html#abs-1803-06632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W138971572","https://openalex.org/W1482810056","https://openalex.org/W1570018751","https://openalex.org/W1779881208","https://openalex.org/W1880548338","https://openalex.org/W1951217842","https://openalex.org/W1974054882","https://openalex.org/W1982243787","https://openalex.org/W2000846246","https://openalex.org/W2021496101","https://openalex.org/W2044306058","https://openalex.org/W2064853889","https://openalex.org/W2076428730","https://openalex.org/W2083751087","https://openalex.org/W2085496100","https://openalex.org/W2115482638","https://openalex.org/W2161591307","https://openalex.org/W2164551143","https://openalex.org/W2169474320","https://openalex.org/W2497151817","https://openalex.org/W2534323040","https://openalex.org/W2588264602","https://openalex.org/W3150565720"],"related_works":["https://openalex.org/W2811213312","https://openalex.org/W3095461723","https://openalex.org/W2372573929","https://openalex.org/W2136458912","https://openalex.org/W2753231694","https://openalex.org/W2380885997","https://openalex.org/W2106077082","https://openalex.org/W7096391053","https://openalex.org/W2001082095","https://openalex.org/W1840507838"],"abstract_inverted_index":{"In":[0,92],"this":[1],"paper":[2],"we":[3,94],"present":[4],"the":[5,15,35,53,57,61,82,90,98,102,130,135,146],"GFP-growth":[6,54,136],"(Guided":[7],"FP-growth)":[8],"algorithm,":[9],"a":[10,18,68,73,108],"novel":[11],"method":[12],"for":[13,60,67,88,137],"finding":[14],"count":[16],"of":[17,21,39,70,81,85,100,145],"given":[19],"list":[20],"itemsets":[22,38],"in":[23,96,112],"large":[24],"data.":[25,158],"Unlike":[26],"FP-growth,":[27],"our":[28],"algorithm":[29,55],"is":[30],"designed":[31],"to":[32],"focus":[33],"on":[34],"specific":[36],"multiple":[37],"interest":[40],"and":[41,45,123,126,149,156],"hence":[42],"its":[43,151],"time":[44],"memory":[46],"costs":[47],"are":[48],"better.":[49],"We":[50,64,128,140],"prove":[51,141],"that":[52,66,110,133],"yields":[56],"exact":[58],"frequency-counts":[59],"required":[62],"itemsets.":[63],"show":[65],"number":[69],"different":[71],"problems,":[72],"solution":[74],"can":[75],"be":[76],"devised":[77],"which":[78],"takes":[79],"advantage":[80],"efficient":[83],"implementation":[84],"multi-targeted":[86],"mining":[87],"boosting":[89,138],"performance.":[91,139],"particular,":[93],"study":[95],"detail":[97],"problem":[99],"generating":[101],"minority-class":[103],"rules":[104],"from":[105],"imbalanced":[106],"data,":[107],"scenario":[109],"appears":[111],"many":[113],"real-life":[114],"domains":[115],"such":[116],"as":[117],"medical":[118],"applications,":[119],"failure":[120],"prediction,":[121],"network":[122],"cyber":[124],"security,":[125],"maintenance.":[127],"develop":[129],"Minority-Report":[131,147],"Algorithm":[132,148],"uses":[134],"some":[142],"theoretical":[143],"properties":[144],"demonstrate":[150],"superior":[152],"performance":[153],"using":[154],"simulations":[155],"real":[157]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-09-26T08:19:03.415552","created_date":"2018-03-29T00:00:00"}