{"id":"https://openalex.org/W4254187087","doi":"https://doi.org/10.3233/ida-140680","title":"Editorial","display_name":"Editorial","publication_year":2014,"publication_date":"2014-10-29","ids":{"openalex":"https://openalex.org/W4254187087","doi":"https://doi.org/10.3233/ida-140680"},"language":"es","primary_location":{"id":"doi:10.3233/ida-140680","is_oa":true,"landing_page_url":"https://doi.org/10.3233/ida-140680","pdf_url":"https://content.iospress.com:443/download/intelligent-data-analysis/ida00680?id=intelligent-data-analysis%2Fida00680","source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"},"type":"editorial","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://content.iospress.com:443/download/intelligent-data-analysis/ida00680?id=intelligent-data-analysis%2Fida00680","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019909538","display_name":"A. Famili","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"A. Famili","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5019909538"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65066805,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"6","first_page":"995","last_page":"996"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.5181999802589417,"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":0.5181999802589417,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.023600000888109207,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.01899999938905239,"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/computer-science","display_name":"Computer science","score":0.2722848951816559}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2722848951816559}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ida-140680","is_oa":true,"landing_page_url":"https://doi.org/10.3233/ida-140680","pdf_url":"https://content.iospress.com:443/download/intelligent-data-analysis/ida00680?id=intelligent-data-analysis%2Fida00680","source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3233/ida-140680","is_oa":true,"landing_page_url":"https://doi.org/10.3233/ida-140680","pdf_url":"https://content.iospress.com:443/download/intelligent-data-analysis/ida00680?id=intelligent-data-analysis%2Fida00680","source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4254187087.pdf","grobid_xml":"https://content.openalex.org/works/W4254187087.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W20957060","https://openalex.org/W36968892","https://openalex.org/W34038126","https://openalex.org/W56463007","https://openalex.org/W34471911","https://openalex.org/W42359850","https://openalex.org/W33680886","https://openalex.org/W33133490","https://openalex.org/W37563523","https://openalex.org/W53607818"],"abstract_inverted_index":{"This":[0],"issue":[1,149,414],"of":[2,7,10,27,45,71,77,79,93,101,119,147,153,187,242,244,294,311,324,328,396,415,429,441,459,477],"IDA":[3],"journal,":[4],"the":[5,19,25,42,85,90,126,144,151,167,235,240,276,309,331,336,393,409,413,439],"last":[6,475],"2014,":[8],"consists":[9],"eleven":[11],"articles,":[12],"all":[13,194],"covering":[14],"various":[15,419],"topics":[16],"related":[17],"to":[18,179,192,274,381,446,528,537],"theoretical":[20],"and":[21,39,54,67,104,159,214,228,264,271,314,318,322,377,404,421,433,451,482,502],"applied":[22],"research":[23],"in":[24,41,50,75,84,89,138,143,156,162,166,247,330,356,373,388,408,418,456,471],"field":[26],"Intelligent":[28],"Data":[29],"Analysis.The":[30],"first":[31,43],"four":[32],"articles":[33,306],"are":[34,82,279,283,307,349,379],"on":[35,225,254,261,308,498,518],"frequent":[36,72,236,255,269],"pattern":[37],"discovery.Muino":[38],"Borgelt,":[40],"article":[44,92,146,390,411,476],"this":[46,94,148,352,389,397,478],"issue,":[47,95],"discuss":[48,96,150,320,412],"synchrony":[49],"neuronal":[51],"spike":[52,60],"trains":[53],"present":[55,105],"a":[56,65,106,110,185,292,341,423,427,491,525],"framework":[57],"for":[58,69,98,116,132,205,301,351,359,506],"characterizing":[59],"synchrony.In":[61],"addition,":[62],"they":[63,249],"introduce":[64,490],"methodology":[66],"algorithms":[68,224],"identification":[70],"parallel":[73],"episodes":[74],"sequences":[76],"events.Results":[78],"their":[80,199,215,220,357,465,513],"evaluation":[81],"presented":[83],"paper.Line":[86],"et":[87,141,231,406],"al.,":[88],"next":[91,304,410],"methods":[97],"efficient":[99,117],"updating":[100],"sequential":[102,120],"patterns":[103,267],"study":[107],"that":[108,113,125,161,174,219,282,392,425,464,495],"extends":[109],"sequential-pattern-tree":[111],"algorithm":[112,128,204,221],"is":[114,259,289,326,335,371,400,426,444,484,496,504,516],"targeted":[115],"handling":[118,134,315],"patterns.Their":[121],"experimental":[122,216,461],"results":[123,217,300,355,462],"show":[124,218],"proposed":[127,287,372,398,466,514],"has":[129],"good":[130],"performance":[131],"incrementally":[133],"newly":[135],"inserted":[136],"transactions":[137,281],"large":[139],"databases.Ryang":[140],"al.":[142,232],"third":[145],"complexity":[152],"rule":[154,176],"mining":[155,177,206],"items":[157,165],"database":[158,168],"argue":[160],"real":[163,227],"world,":[164],"have":[169],"different":[170],"nature.This":[171],"point":[172,241],"emphasizes":[173],"association":[175],"needs":[178],"consider":[180],"multiple":[181,211],"minimum":[182,212],"supports":[183,213],"as":[184,345],"result":[186],"which":[188,248,278,354,374,521],"one":[189],"can":[190],"expect":[191],"discover":[193],"item":[195,237],"rules":[196],"by":[197,480],"reflecting":[198],"characteristics.The":[200],"authors":[201,489],"propose":[202,250,422],"an":[203,251],"high":[207],"utility":[208],"itemsets":[209,273],"with":[210,298,402,500],"outperforms":[222],"comparing":[223],"both":[226,448],"synthetic":[229],"datasets.Abdul-Kadir":[230],"look":[233],"at":[234],"sets":[238,297],"from":[239,268],"view":[243],"outlier":[245,302],"detection":[246],"approach":[252,399,467,515],"based":[253,260,497,517],"negative":[256,272],"itemsets.The":[257],"idea":[258,288,437],"generating":[262],"knowledge":[263],"discovering":[265],"interesting":[266,299],"positive":[270],"identify":[275],"outliers,":[277],"essentially":[280],"common":[284],"or":[285],"rare.Their":[286],"evaluated":[290],"using":[291,510],"set":[293],"UCI":[295],"data":[296,312,339,473],"detection.The":[303],"three":[305,519],"topic":[310,369],"pre-processing":[313],"imbalanced":[316,472,487,508],"data.Than":[317],"Ho":[319],"diversity":[321,403],"log-normality":[323,334],"data.Diversity":[325],"representative":[327],"variations":[329],"attributes":[332],"while":[333],"situation":[337],"where":[338],"follows":[340],"log-normal":[342],"distribution.Methods":[343],"such":[344],"Latent":[346],"Dirichlet":[347,367],"Allocation":[348],"investigated":[350],"problem":[353],"limitations":[358],"modeling":[360],"these":[361,442],"two":[362,443,534],"issues.A":[363],"vibrational":[364],"method,":[365],"called":[366],"lognormal":[368],"model,":[370],"model":[375],"learning":[376],"inference":[378],"reduced":[380],"solving":[382],"complex":[383],"optimizational":[384],"problems.The":[385],"experiments":[386],"reported":[387],"suggest":[391],"predictive":[394],"power":[395],"consistent":[401],"log-normality.Cao":[405],"al":[407],"class":[416,449,453],"imbalance":[417,450,454],"forms":[420],"method":[424],"combination":[428,440],"hybrid":[430],"probabilistic":[431],"sampling":[432,501],"diverse":[434],"random":[435],"subspace.The":[436],"behind":[438],"apparently":[445],"solve":[447],"within":[452],"problems":[455],"certain":[457],"classes":[458],"data.Their":[460],"demonstrate":[463],"performs":[468],"very":[469],"well":[470],"sets.The":[474],"group":[479],"Prachuabsupakij":[481],"Soonthornphisaj":[483],"also":[485],"about":[486],"data.The":[488],"new":[492],"resampling":[493,535],"technique":[494],"clustering":[499,526],"it":[503],"primarily":[505],"multiclass":[507],"classification":[509],"ensemble":[511],"techniques.Essentially":[512],"steps":[520],"consist":[522],"of:":[523],"(i)":[524],"step":[527],"create":[529],"training":[530],"sets,":[531],"(ii)":[532],"applying":[533],"techniques":[536]},"counts_by_year":[],"updated_date":"2026-03-03T08:47:05.690250","created_date":"2022-05-12T00:00:00"}