{"id":"https://openalex.org/W1992704624","doi":"https://doi.org/10.1142/s0218001409007314","title":"MINING FREQUENT ITEMSETS IN DISTORTED DATABASES WITH GRANULAR COMPUTING","display_name":"MINING FREQUENT ITEMSETS IN DISTORTED DATABASES WITH GRANULAR COMPUTING","publication_year":2009,"publication_date":"2009-06-01","ids":{"openalex":"https://openalex.org/W1992704624","doi":"https://doi.org/10.1142/s0218001409007314","mag":"1992704624"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001409007314","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001409007314","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-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/A5100340094","display_name":"Jinlong Wang","orcid":"https://orcid.org/0000-0001-5679-1495"},"institutions":[{"id":"https://openalex.org/I44468530","display_name":"Qingdao University of Technology","ror":"https://ror.org/01qzc0f54","country_code":"CN","type":"education","lineage":["https://openalex.org/I44468530"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JINLONG WANG","raw_affiliation_strings":["School of Computer Engineering, Qingdao Technological University, Qingdao, Shandong 266033, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Qingdao Technological University, Qingdao, Shandong 266033, P. R. China","institution_ids":["https://openalex.org/I44468530"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100513265","display_name":"Congfu Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"CONGFU XU","raw_affiliation_strings":["Institute of Artificial Intelligence, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100438769","display_name":"Gang Li","orcid":"https://orcid.org/0000-0003-1583-641X"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"GANG LI","raw_affiliation_strings":["School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, VIC 3125, Australia","School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, Vic 3125, Australia#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, VIC 3125, Australia","institution_ids":["https://openalex.org/I149704539"]},{"raw_affiliation_string":"School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, Vic 3125, Australia#TAB#","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.1292722,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"23","issue":"04","first_page":"825","last_page":"846"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9983000159263611,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9972000122070312,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bitmap","display_name":"Bitmap","score":0.891269862651825},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7226780652999878},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.7171016335487366},{"id":"https://openalex.org/keywords/granular-computing","display_name":"Granular computing","score":0.5839389562606812},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5425018668174744},{"id":"https://openalex.org/keywords/efficient-algorithm","display_name":"Efficient algorithm","score":0.45408231019973755},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.4315493404865265},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33410149812698364},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24736973643302917},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.11720001697540283}],"concepts":[{"id":"https://openalex.org/C3115412","wikidata":"https://www.wikidata.org/wiki/Q1194708","display_name":"Bitmap","level":2,"score":0.891269862651825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7226780652999878},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7171016335487366},{"id":"https://openalex.org/C17209119","wikidata":"https://www.wikidata.org/wiki/Q5596712","display_name":"Granular computing","level":3,"score":0.5839389562606812},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5425018668174744},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.45408231019973755},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.4315493404865265},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33410149812698364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24736973643302917},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.11720001697540283}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001409007314","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001409007314","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1484413656","https://openalex.org/W1490134282","https://openalex.org/W1517636450","https://openalex.org/W1527854642","https://openalex.org/W1533530351","https://openalex.org/W1535372778","https://openalex.org/W1563151522","https://openalex.org/W1566420918","https://openalex.org/W1750350926","https://openalex.org/W1969019410","https://openalex.org/W1970213392","https://openalex.org/W2036999870","https://openalex.org/W2052608046","https://openalex.org/W2085029196","https://openalex.org/W2094018592","https://openalex.org/W2110893883","https://openalex.org/W2117315444","https://openalex.org/W2122240322","https://openalex.org/W2128906841","https://openalex.org/W2129555316","https://openalex.org/W2130099852","https://openalex.org/W2136593687","https://openalex.org/W2139228999","https://openalex.org/W2142280242","https://openalex.org/W2153676086","https://openalex.org/W2153932493","https://openalex.org/W2156026066","https://openalex.org/W2158454296","https://openalex.org/W2161067131","https://openalex.org/W2166559705"],"related_works":["https://openalex.org/W2350456333","https://openalex.org/W2101993108","https://openalex.org/W2356608866","https://openalex.org/W2355840328","https://openalex.org/W1975966184","https://openalex.org/W2364393392","https://openalex.org/W1829880586","https://openalex.org/W2752554601","https://openalex.org/W2022767186","https://openalex.org/W1992704624"],"abstract_inverted_index":{"Data":[0],"perturbation":[1],"is":[2,62,67],"a":[3],"popular":[4,147],"method":[5],"to":[6,17,22,33,49,69],"achieve":[7],"privacy-preserving":[8],"data":[9],"mining.":[10],"However,":[11],"distorted":[12,43],"databases":[13,138],"bring":[14,111],"enormous":[15],"overheads":[16],"mining":[18,39],"algorithms":[19],"as":[20],"compared":[21],"original":[23],"databases.":[24,44],"In":[25],"this":[26],"paper,":[27],"we":[28],"present":[29],"the":[30,35,51,57,81,95,105,108,117,120,141,146,152,155],"GrC-FIM":[31,143],"algorithm":[32,144,149],"address":[34],"efficiency":[36,106,153],"problem":[37],"in":[38,53,126,150],"frequent":[40],"itemsets":[41,73,86],"from":[42,91],"Two":[45],"measures":[46],"are":[47],"introduced":[48],"overcome":[50],"weakness":[52],"existing":[54],"work:":[55],"firstly,":[56],"concept":[58],"of":[59,84,107],"independent":[60,75],"granule":[61,65],"introduced,":[63],"and":[64,74,110,136,154],"inference":[66],"used":[68],"distinguish":[70],"between":[71],"non-independent":[72,85],"itemsets.":[76],"We":[77],"further":[78],"prove":[79],"that":[80,94,140],"support":[82,121,156],"counts":[83,122],"can":[87,99,123],"be":[88,100,124],"directly":[89],"derived":[90],"subitemsets,":[92],"so":[93],"error-prone":[96],"reconstruction":[97,158],"process":[98],"avoided.":[101],"This":[102],"could":[103],"improve":[104],"algorithm,":[109],"more":[112],"accurate":[113],"results;":[114],"secondly,":[115],"through":[116],"granular-bitmap":[118],"representation,":[119],"calculated":[125],"an":[127],"efficient":[128],"way.":[129],"The":[130],"empirical":[131],"results":[132],"on":[133],"representative":[134],"synthetic":[135],"real-world":[137],"indicate":[139],"proposed":[142],"outperforms":[145],"EMASK":[148],"both":[151],"count":[157],"accuracy.":[159]},"counts_by_year":[{"year":2016,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
