{"id":"https://openalex.org/W2130525844","doi":"https://doi.org/10.1109/icdm.2003.1250947","title":"Segmenting customer transactions using a pattern-based clustering approach","display_name":"Segmenting customer transactions using a pattern-based clustering approach","publication_year":2004,"publication_date":"2004-04-23","ids":{"openalex":"https://openalex.org/W2130525844","doi":"https://doi.org/10.1109/icdm.2003.1250947","mag":"2130525844"},"language":"en","primary_location":{"id":"doi:10.1109/icdm.2003.1250947","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2003.1250947","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Third IEEE International Conference on Data Mining","raw_type":"proceedings-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/A5028003079","display_name":"Yiling Yang","orcid":"https://orcid.org/0000-0001-9771-850X"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Y. Yang","raw_affiliation_strings":["Operations and Information Management Department, The Wharton School, University of Pennsylvania, USA","Dept. of Operations & Inf. Manage., Pennsylvania Univ., Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Operations and Information Management Department, The Wharton School, University of Pennsylvania, USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Dept. of Operations & Inf. Manage., Pennsylvania Univ., Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050411634","display_name":"Balaji Padmanabhan","orcid":"https://orcid.org/0000-0002-3498-0778"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Balaji Padmanabhan","raw_affiliation_strings":["Operations and Information Management Department, The Wharton School, University of Pennsylvania, USA","Dept. of Operations & Inf. Manage., Pennsylvania Univ., Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Operations and Information Management Department, The Wharton School, University of Pennsylvania, USA","institution_ids":["https://openalex.org/I79576946"]},{"raw_affiliation_string":"Dept. of Operations & Inf. Manage., Pennsylvania Univ., Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5028003079"],"corresponding_institution_ids":["https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":6.6604,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.96319753,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9993000030517578,"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":0.9993000030517578,"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/T12384","display_name":"Customer churn and segmentation","score":0.9926999807357788,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9883000254631042,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8428812026977539},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7606042623519897},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.7424837350845337},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.6078125238418579},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5779106616973877},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5191135406494141},{"id":"https://openalex.org/keywords/transaction-data","display_name":"Transaction data","score":0.4921550154685974},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39559128880500793},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.2119966447353363},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.08862277865409851},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.07713988423347473}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8428812026977539},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7606042623519897},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.7424837350845337},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.6078125238418579},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5779106616973877},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5191135406494141},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.4921550154685974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39559128880500793},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2119966447353363},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.08862277865409851},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.07713988423347473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm.2003.1250947","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2003.1250947","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Third IEEE International Conference on Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W60856054","https://openalex.org/W1553696291","https://openalex.org/W1983524036","https://openalex.org/W1984075651","https://openalex.org/W1995502855","https://openalex.org/W2011832962","https://openalex.org/W2041674806","https://openalex.org/W2047375989","https://openalex.org/W2047555270","https://openalex.org/W2057712948","https://openalex.org/W2081980673","https://openalex.org/W2099581008","https://openalex.org/W2120970098","https://openalex.org/W2788737710","https://openalex.org/W3139328003","https://openalex.org/W4245412318","https://openalex.org/W4256515882","https://openalex.org/W6602452485","https://openalex.org/W6633402183","https://openalex.org/W6660741141","https://openalex.org/W6791582515"],"related_works":["https://openalex.org/W2592395359","https://openalex.org/W2045342254","https://openalex.org/W2535231171","https://openalex.org/W2142182663","https://openalex.org/W1501331687","https://openalex.org/W4255512592","https://openalex.org/W2501551404","https://openalex.org/W2326647871","https://openalex.org/W4205247302","https://openalex.org/W2468652214"],"abstract_inverted_index":{"Grouping":[0],"customer":[1,20,50],"transactions":[2,74],"into":[3],"categories":[4],"helps":[5],"understand":[6],"customers":[7],"better.":[8],"The":[9,31],"marketing":[10],"literature":[11,34],"has":[12,35],"concentrated":[13],"on":[14,23,62],"identifying":[15],"important":[16],"segmentation":[17],"variables":[18],"(e.g.":[19],"loyalty)":[21],"and":[22,26,66],"using":[24,44],"clustering":[25,38,46,111],"mixture":[27],"models":[28],"for":[29,40],"segmentation.":[30,41],"data":[32,60,102],"mining":[33],"provided":[36],"various":[37],"algorithms":[39],"We":[42,52,94],"investigate":[43],"\"pattern-based\"":[45],"approaches":[47],"to":[48,84],"grouping":[49],"transactions.":[51,113],"argue":[53],"that":[54,72,76,103,105],"there":[55],"are":[56,87],"clusters":[57],"in":[58],"transaction":[59],"based":[61],"natural":[63],"behavioral":[64],"patterns,":[65],"present":[67,95],"a":[68,108],"new":[69],"technique,":[70],"YACA,":[71],"groups":[73],"such":[75],"itemsets":[77],"generated":[78,91],"from":[79,89,92,98],"each":[80,85],"cluster,":[81],"while":[82],"similar":[83],"other,":[86],"different":[88],"ones":[90],"others.":[93],"experimental":[96],"results":[97],"user-centric":[99],"Web":[100],"usage":[101],"demonstrates":[104],"YACA":[106],"generates":[107],"highly":[109],"effective":[110],"of":[112]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}