{"id":"https://openalex.org/W2097132425","doi":"https://doi.org/10.7763/ijcte.2013.v5.811","title":"Customer Segmentation Using Clustering and Data Mining Techniques","display_name":"Customer Segmentation Using Clustering and Data Mining Techniques","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2097132425","doi":"https://doi.org/10.7763/ijcte.2013.v5.811","mag":"2097132425"},"language":"en","primary_location":{"id":"doi:10.7763/ijcte.2013.v5.811","doi":"https://doi.org/10.7763/ijcte.2013.v5.811","is_oa":false,"landing_page_url":"https://doi.org/10.7763/ijcte.2013.v5.811","pdf_url":null,"source":{"id":"https://openalex.org/S4210186493","display_name":"International Journal of Computer Theory and Engineering","issn_l":"1793-8201","issn":["1793-8201"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Theory and Engineering","raw_type":"journal-article"},"volume":null,"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/A5032457708","display_name":"Kishana Ram Kashwan","orcid":"https://orcid.org/0000-0002-5708-5696","observed_orcids":["https://orcid.org/0000-0002-5708-5696"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kishana R. Kashwan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5004591834","display_name":"C. M. Velu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"C. M. Velu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9905,"has_fulltext":false,"cited_by_count":75,"citation_normalized_percentile":{"value":0.87093363,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"856","last_page":"861"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.5464000105857849,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.5464000105857849,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.38989999890327454,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.05310000106692314,"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/customer-segmentation","display_name":"customer segmentation","score":1.0},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"K-means clustering","score":0.9929999709129333},{"id":"https://openalex.org/keywords/cluster-stability","display_name":"cluster stability","score":0.7139999866485596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8891832232475281},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7666886448860168},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.5536714196205139},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5521430969238281},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5283435583114624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41612571477890015},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32869797945022583},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.06628403067588806},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.046568334102630615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.7763/ijcte.2013.v5.811","doi":"https://doi.org/10.7763/ijcte.2013.v5.811","is_oa":false,"landing_page_url":"https://doi.org/10.7763/ijcte.2013.v5.811","pdf_url":null,"source":{"id":"https://openalex.org/S4210186493","display_name":"International Journal of Computer Theory and Engineering","issn_l":"1793-8201","issn":["1793-8201"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Theory and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1578468649","https://openalex.org/W1790954942","https://openalex.org/W1986788050","https://openalex.org/W2022266106","https://openalex.org/W2086959852","https://openalex.org/W2099242680","https://openalex.org/W2103718624","https://openalex.org/W2117250207","https://openalex.org/W2139850885","https://openalex.org/W2161160262","https://openalex.org/W2171852577","https://openalex.org/W2336630152","https://openalex.org/W2950437976"],"related_works":["https://openalex.org/W4401758464","https://openalex.org/W2373958806","https://openalex.org/W4402958157","https://openalex.org/W4377704418","https://openalex.org/W4206675084","https://openalex.org/W4390487504","https://openalex.org/W7036858889","https://openalex.org/W4321488455","https://openalex.org/W2799494502","https://openalex.org/W2022756714"],"abstract_inverted_index":{"Clustering":[0],"technique":[1,35],"is":[2,10,28],"critically":[3],"important":[4],"step":[5],"in":[6,19,55],"data":[7,72],"mining":[8],"process.It":[9],"a":[11,29,41,48,92],"multivariate":[12],"procedure":[13],"quite":[14,151],"suitable":[15],"for":[16,47,105],"segmentation":[17,77],"applications":[18],"the":[20,80,131,134,148],"market":[21,51],"forecasting":[22],"and":[23,36,44,74,89,153],"planning":[24],"research.This":[25],"research":[26],"paper":[27],"comprehensive":[30],"report":[31],"of":[32,82,94,98,133],"k-means":[33],"clustering":[34],"SPSS":[37],"Tool":[38],"to":[39,52,129,138],"develop":[40],"real":[42],"time":[43],"online":[45],"system":[46],"particular":[49],"super":[50],"predict":[53],"sales":[54,71,140],"various":[56],"annual":[57],"seasonal":[58],"cycles.The":[59],"model":[60,85],"developed":[61],"was":[62,86,118,125],"an":[63],"intelligent":[64],"tool":[65],"which":[66,107],"received":[67],"inputs":[68],"directly":[69],"from":[70],"records":[73],"automatically":[75],"updated":[76],"statistics":[78,141,146],"at":[79],"end":[81],"day's":[83],"business.The":[84],"successfully":[87],"implemented":[88],"tested":[90,104],"over":[91],"period":[93],"three":[95],"months.A":[96],"total":[97],"n":[99],"=":[100,113],"2138,":[101],"customer,":[102],"were":[103,108,142,150],"observations":[106],"then":[109],"divided":[110],"into":[111],"k":[112],"4":[114],"similar":[115],"groups.The":[116],"classification":[117],"based":[119],"on":[120],"nearest":[121],"mean.An":[122],"ANOVA":[123],"analysis":[124],"also":[126],"carried":[127],"out":[128],"test":[130],"stability":[132],"clusters.The":[135],"actual":[136],"day":[137,139],"compared":[143],"with":[144],"predicted":[145],"by":[147],"model.Results":[149],"encouraging":[152],"had":[154],"shown":[155],"high":[156],"accuracy.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-10-22T09:42:04.148104","created_date":"2016-06-24T00:00:00"}