{"id":"https://openalex.org/W7106686671","doi":"https://doi.org/10.5281/zenodo.17709840","title":"PyAerial: Scalable association rule mining from tabular data","display_name":"PyAerial: Scalable association rule mining from tabular data","publication_year":2025,"publication_date":"2025-11-25","ids":{"openalex":"https://openalex.org/W7106686671","doi":"https://doi.org/10.5281/zenodo.17709840"},"language":null,"primary_location":{"id":"doi:10.5281/zenodo.17709840","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17709840","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.17709840","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Karabulut, Erkan","orcid":"https://orcid.org/0000-0003-2710-7951"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Karabulut, Erkan","raw_affiliation_strings":["University of Amsterdam"],"raw_orcid":"https://orcid.org/0000-0003-2710-7951","affiliations":[{"raw_affiliation_string":"University of Amsterdam","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Groth, Paul","orcid":"https://orcid.org/0000-0003-0183-6910"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Groth, Paul","raw_affiliation_strings":["University of Amsterdam"],"raw_orcid":"https://orcid.org/0000-0003-0183-6910","affiliations":[{"raw_affiliation_string":"University of Amsterdam","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"last","author":{"id":null,"display_name":"Degeler, Victoria","orcid":"https://orcid.org/0000-0001-7054-3770"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Degeler, Victoria","raw_affiliation_strings":["University of Amsterdam"],"raw_orcid":"https://orcid.org/0000-0001-7054-3770","affiliations":[{"raw_affiliation_string":"University of Amsterdam","institution_ids":["https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I887064364"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.8604999780654907},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6403999924659729},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5809999704360962},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5157999992370605},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42239999771118164},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4023999869823456},{"id":"https://openalex.org/keywords/k-optimal-pattern-discovery","display_name":"K-optimal pattern discovery","score":0.40059998631477356},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.3479999899864197},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.3418000042438507}],"concepts":[{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.8604999780654907},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.7480000257492065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7421000003814697},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6403999924659729},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5809999704360962},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48410001397132874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4417000114917755},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4023999869823456},{"id":"https://openalex.org/C105445830","wikidata":"https://www.wikidata.org/wiki/Q6322855","display_name":"K-optimal pattern discovery","level":3,"score":0.40059998631477356},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.3479999899864197},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C149271511","wikidata":"https://www.wikidata.org/wiki/Q1417149","display_name":"Rule-based system","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28700000047683716},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C23906176","wikidata":"https://www.wikidata.org/wiki/Q727515","display_name":"Affinity analysis","level":3,"score":0.2831000089645386},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.28110000491142273},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C81440476","wikidata":"https://www.wikidata.org/wiki/Q513511","display_name":"Apriori algorithm","level":3,"score":0.2768999934196472},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C3746660","wikidata":"https://www.wikidata.org/wiki/Q1068763","display_name":"Rule of inference","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.17709840","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17709840","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.5281/zenodo.17709840","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.17709840","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Scalable":[0,24,228],"association":[1,18,30,229],"rule":[2,19,37,74,230],"mining":[3,20,75,231],"from":[4,32,198,232],"tabular":[5,34,233],"data":[6],"using":[7,63],"the":[8,64],"Aerial":[9],"neurosymbolic":[10,184],"method.":[11],"PyAerial":[12,175],"provides":[13],"a":[14],"comprehensive":[15],"toolkit":[16],"for":[17,90,121,129,153],"with":[21,60,69,86,151],"advanced":[22],"capabilities:":[23],"Rule":[25,147,196],"Mining":[26,55,197],"-":[27,42,56,72,83,96,115,126,137,149,161],"Efficiently":[28],"mine":[29],"rules":[31,85],"large":[33,133],"datasets":[35,134],"without":[36],"explosion":[38],"Automatic":[39],"Quality":[40],"Metrics":[41,136],"Rules":[43,82],"include":[44],"support,":[45],"confidence,":[46,139],"Zhang's":[47,142],"metric,":[48,143],"and":[49,119,156,168,211],"more":[50],"calculated":[51],"automatically":[52],"Frequent":[53],"Itemset":[54],"Generate":[57],"frequent":[58],"itemsets":[59],"support":[61],"values":[62],"same":[65],"neural":[66],"approach":[67],"ARM":[68],"Item":[70],"Constraints":[71],"Focus":[73],"on":[76,132,208],"specific":[77],"features":[78],"of":[79,203],"interest":[80],"Classification":[81],"Extract":[84],"target":[87],"class":[88],"labels":[89],"interpretable":[91],"inference":[92],"Numerical":[93],"Data":[94],"Support":[95],"8":[97],"built-in":[98],"discretization":[99],"methods":[100],"(unsupervised:":[101],"equal-frequency,":[102],"equal-width,":[103],"k-means,":[104],"quantile,":[105],"custom":[106],"bins;":[107],"supervised:":[108],"entropy-based,":[109],"ChiMerge,":[110],"decision":[111],"tree)":[112],"Customizable":[113],"Architectures":[114],"Fine-tune":[116],"autoencoder":[117],"layers":[118],"dimensions":[120],"optimal":[122],"performance":[123],"GPU":[124],"Acceleration":[125],"Leverage":[127],"CUDA":[128],"faster":[130],"training":[131],"Comprehensive":[135],"Support,":[138],"lift,":[140],"conviction,":[141],"Yule's":[144],"Q,":[145],"interestingness":[146],"Visualization":[148],"Integrate":[150],"NiaARM":[152],"scatter":[154],"plots":[155],"visual":[157],"analysis":[158],"Flexible":[159],"Training":[160],"Adjust":[162],"epochs,":[163],"learning":[164],"rate,":[165],"batch":[166],"size,":[167],"noise":[169],"factors":[170],"CITATION:":[171],"If":[172],"you":[173],"use":[174],"in":[176],"your":[177],"research,":[178],"please":[179],"cite":[180],"our":[181],"papers:":[182],"The":[183,204,217],"method":[185],"paper:Karabulut,":[186,219],"E.,":[187,220],"Groth,":[188,221],"P.,":[189,222],"&":[190,223],"Degeler,":[191,224],"V.":[192,225],"(2025).":[193,226],"Neurosymbolic":[194,209],"Association":[195],"Tabular":[199],"Data.":[200],"In":[201],"Proceedings":[202],"19th":[205],"International":[206],"Conference":[207],"Learning":[210],"Reasoning":[212],"(NeSy":[213],"2025),":[214],"PMLR":[215],"284:565-588.https://proceedings.mlr.press/v284/karabulut25a.html":[216],"software":[218],"PyAerial:":[227],"data.":[234],"SoftwareX,":[235],"31,":[236],"102341.https://doi.org/10.1016/j.softx.2025.102341":[237]},"counts_by_year":[],"updated_date":"2025-11-27T01:16:37.896743","created_date":"2025-11-27T00:00:00"}
