{"id":"https://openalex.org/W7127047524","doi":"https://doi.org/10.1080/03610926.2026.2618623","title":"An empirical analysis toward mining association rules for market basket analysis through statistical measures","display_name":"An empirical analysis toward mining association rules for market basket analysis through statistical measures","publication_year":2026,"publication_date":"2026-02-01","ids":{"openalex":"https://openalex.org/W7127047524","doi":"https://doi.org/10.1080/03610926.2026.2618623"},"language":"en","primary_location":{"id":"doi:10.1080/03610926.2026.2618623","doi":"https://doi.org/10.1080/03610926.2026.2618623","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610926.2026.2618623","pdf_url":null,"source":{"id":"https://openalex.org/S128350725","display_name":"Communication in Statistics- Theory and Methods","issn_l":"0361-0926","issn":["0361-0926","1532-415X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","anvur-scientific","cwts-core","dongbi-d","doyens","jufo-1","ki-jl-1","norway-1","poland-40","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Theory and Methods","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W7127047524"],"child_works_count":0,"type":"article","is_traditional":true,"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/A5067967516","display_name":"Harish Kumar Pamnani","orcid":"https://orcid.org/0000-0002-8215-7129","observed_orcids":["https://orcid.org/0000-0002-8215-7129"]},"institutions":[{"id":"https://openalex.org/I73779912","display_name":"Manipal University Jaipur","ror":"https://ror.org/040h76494","country_code":"IN","type":"education","lineage":["https://openalex.org/I73779912"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Harish Kumar Pamnani","raw_affiliation_strings":["Department of Computer Applications, Manipal University Jaipur"],"raw_orcid":"https://orcid.org/0000-0002-8215-7129","affiliations":[{"raw_affiliation_string":"Department of Computer Applications, Manipal University Jaipur","institution_ids":["https://openalex.org/I73779912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057299766","display_name":"Linesh Raja","orcid":"https://orcid.org/0000-0002-3663-1184","observed_orcids":["https://orcid.org/0000-0002-3663-1184"]},"institutions":[{"id":"https://openalex.org/I73779912","display_name":"Manipal University Jaipur","ror":"https://ror.org/040h76494","country_code":"IN","type":"education","lineage":["https://openalex.org/I73779912"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Linesh Raja","raw_affiliation_strings":["Department of Computer Applications, Manipal University Jaipur"],"raw_orcid":"https://orcid.org/0000-0002-3663-1184","affiliations":[{"raw_affiliation_string":"Department of Computer Applications, Manipal University Jaipur","institution_ids":["https://openalex.org/I73779912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059143107","display_name":"Thom Ives","orcid":"https://orcid.org/0000-0001-7142-7034","observed_orcids":["https://orcid.org/0000-0001-7142-7034","https://orcid.org/0009-0009-8536-5976"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thom Ives","raw_affiliation_strings":["Echo Global Logistic"],"raw_orcid":"https://orcid.org/0009-0009-8536-5976","affiliations":[{"raw_affiliation_string":"Echo Global Logistic","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5057299766"],"corresponding_institution_ids":["https://openalex.org/I73779912"],"apc_list":null,"apc_paid":null,"fwci":3.5612,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.89815732,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"55","issue":"14","first_page":"4740","last_page":"4757"},"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/association-rule-mining","display_name":"association rule mining","score":0.9929999709129333},{"id":"https://openalex.org/keywords/market-basket-analysis","display_name":"market basket analysis","score":1.0},{"id":"https://openalex.org/keywords/apriori-algorithm","display_name":"Apriori algorithm","score":0.9819999933242798},{"id":"https://openalex.org/keywords/fp-growth-algorithm","display_name":"FP-Growth algorithm","score":0.8610000014305115},{"id":"https://openalex.org/keywords/eclat-algorithm","display_name":"Eclat algorithm","score":0.9810000061988831},{"id":"https://openalex.org/keywords/frequent-itemset-mining","display_name":"frequent itemset mining","score":0.878000020980835},{"id":"https://openalex.org/keywords/algorithm-comparison","display_name":"algorithm comparison","score":0.746999979019165},{"id":"https://openalex.org/keywords/execution-time","display_name":"execution time","score":0.5289999842643738},{"id":"https://openalex.org/keywords/memory-usage","display_name":"memory usage","score":0.9909999966621399},{"id":"https://openalex.org/keywords/confidence-intervals","display_name":"confidence intervals","score":0.8479999899864197}],"concepts":[{"id":"https://openalex.org/C23906176","wikidata":"https://www.wikidata.org/wiki/Q727515","display_name":"Affinity analysis","level":3,"score":0.6514000296592712},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5404999852180481},{"id":"https://openalex.org/C2779008359","wikidata":"https://www.wikidata.org/wiki/Q1758955","display_name":"Market basket","level":2,"score":0.5249999761581421},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.43320000171661377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38760000467300415},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.37950000166893005},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.3091000020503998},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2777999937534332},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2599000036716461},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610926.2026.2618623","doi":"https://doi.org/10.1080/03610926.2026.2618623","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610926.2026.2618623","pdf_url":null,"source":{"id":"https://openalex.org/S128350725","display_name":"Communication in Statistics- Theory and Methods","issn_l":"0361-0926","issn":["0361-0926","1532-415X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","anvur-scientific","cwts-core","dongbi-d","doyens","jufo-1","ki-jl-1","norway-1","poland-40","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Theory and Methods","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W2109468178","https://openalex.org/W2773681603","https://openalex.org/W3037168540","https://openalex.org/W3044494610","https://openalex.org/W3138092903","https://openalex.org/W3158574927","https://openalex.org/W3168319761","https://openalex.org/W3171228484","https://openalex.org/W3187536395","https://openalex.org/W3196873783","https://openalex.org/W3206907076","https://openalex.org/W3215387839","https://openalex.org/W4200126102","https://openalex.org/W4200609163","https://openalex.org/W4206290973","https://openalex.org/W4225272929","https://openalex.org/W4229036642","https://openalex.org/W4280541407","https://openalex.org/W4281763922","https://openalex.org/W4313650188","https://openalex.org/W4321349294","https://openalex.org/W4361291445","https://openalex.org/W4381059531","https://openalex.org/W4384786905","https://openalex.org/W4388041882","https://openalex.org/W4388922586","https://openalex.org/W4389220426","https://openalex.org/W4390670370","https://openalex.org/W4399360766","https://openalex.org/W4399645347","https://openalex.org/W4402828583","https://openalex.org/W4403025125","https://openalex.org/W4403091742","https://openalex.org/W4403127796","https://openalex.org/W4403707441","https://openalex.org/W4404524972","https://openalex.org/W4407737898","https://openalex.org/W4408145475","https://openalex.org/W4408408241","https://openalex.org/W4408977541","https://openalex.org/W4409229042","https://openalex.org/W4410362773","https://openalex.org/W4411500002","https://openalex.org/W4413313140","https://openalex.org/W4413441447","https://openalex.org/W4413900617","https://openalex.org/W4415105780"],"related_works":["https://openalex.org/W4410853217","https://openalex.org/W4391857062","https://openalex.org/W2555779076","https://openalex.org/W4386478311","https://openalex.org/W7081665161","https://openalex.org/W4391319496","https://openalex.org/W4320002201","https://openalex.org/W3058368187","https://openalex.org/W2587896742","https://openalex.org/W1998540199"],"abstract_inverted_index":{"The":[0,54,132,145],"value":[1],"of":[2,10,17,30,76,83,118,164],"data":[3,58],"resources":[4,19],"is":[5,27,46,64,150,174,180],"enormous":[6],"within":[7],"the":[8,11,15,28,68,73,116,126,162,175],"framework":[9],"digital":[12],"age,":[13],"and":[14,34,92,103,115,128,134,158,170],"utilization":[16],"these":[18],"has":[20],"given":[21],"rise":[22],"to":[23,40,49,71,124,154],"Data":[24],"Mining.":[25],"It":[26],"practice":[29],"extracting":[31],"helpful":[32],"paradigms":[33],"insightful":[35],"information":[36],"from":[37],"massive":[38],"datasets":[39,110,133],"inform":[41],"recommendations.":[42],"Association":[43],"rule":[44],"mining":[45,57],"a":[47,181],"recipe":[48],"pinpoint":[50],"links":[51],"between":[52],"patterns.":[53],"method":[55],"for":[56,184],"called":[59],"market":[60,86],"basket":[61,87],"analysis":[62,88],"(MBA)":[63],"commonly":[65],"employed":[66],"in":[67,161],"retailing":[69],"industry":[70],"understand":[72],"purchasing":[74],"habits":[75],"consumers.":[77],"This":[78],"paper":[79],"presents":[80],"an":[81],"overview":[82],"three":[84,109],"popular":[85],"algorithms:":[89],"Apriori,":[90],"FP-Growth,":[91],"Eclat.":[93],"Existing":[94],"research":[95],"shows":[96,147],"that":[97,148],"Apriori":[98,149,179],"takes":[99],"more":[100,151],"run":[101,165],"time":[102],"fewer":[104],"frequent":[105,127,167],"itemsets.":[106],"We":[107],"used":[108],"with":[111],"different":[112,122],"transaction":[113],"sizes":[114],"number":[117],"unique":[119],"items":[120],"at":[121],"thresholds":[123],"find":[125],"interesting":[129],"item":[130],"sets.":[131],"results":[135],"are":[136],"tested":[137],"on":[138],"statistical":[139],"tools":[140],"like":[141],"confidence":[142],"intervals,":[143],"variance.":[144],"result":[146],"efficient":[152],"compared":[153],"Frequent":[155],"Pattern":[156],"Growth":[157],"Eclat":[159,173],"algorithms":[160],"context":[163],"time,":[166],"itemset":[168],"finding,":[169],"memory":[171],"consumption.":[172],"highest":[176],"time-taking":[177],"algorithm.":[178],"good":[182],"algorithm":[183],"large,":[185],"sparse":[186],"datasets.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-02-03T00:00:00"}