{"id":"https://openalex.org/W4313131741","doi":"https://doi.org/10.1145/3569966.3570085","title":"A travel customer segmentation method based on improved RFM and k-means++","display_name":"A travel customer segmentation method based on improved RFM and k-means++","publication_year":2022,"publication_date":"2022-10-21","ids":{"openalex":"https://openalex.org/W4313131741","doi":"https://doi.org/10.1145/3569966.3570085"},"language":"en","primary_location":{"id":"doi:10.1145/3569966.3570085","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3569966.3570085","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","raw_type":"proceedings-article"},"type":"conference-paper","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/A5005669451","display_name":"Shaodong Huang","orcid":"https://orcid.org/0000-0002-8420-7070"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaodong Huang","raw_affiliation_strings":["School of Electronic and Information Engineering, Guangxi Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-8420-7070","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Guangxi Normal University, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002946893","display_name":"Sheng Qin","orcid":"https://orcid.org/0000-0001-7348-901X"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Qin","raw_affiliation_strings":["School of Electronic and Information Engineering, Guangxi Normal University, China"],"raw_orcid":"https://orcid.org/0000-0001-7348-901X","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Guangxi Normal University, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073876335","display_name":"Xiaoxiao Jiang","orcid":"https://orcid.org/0000-0002-7264-5479"},"institutions":[{"id":"https://openalex.org/I29739308","display_name":"Guangxi Normal University","ror":"https://ror.org/02frt9q65","country_code":"CN","type":"education","lineage":["https://openalex.org/I29739308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxiao Jiang","raw_affiliation_strings":["Department of Asset Management, Guangxi Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-7264-5479","affiliations":[{"raw_affiliation_string":"Department of Asset Management, Guangxi Normal University, China","institution_ids":["https://openalex.org/I29739308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045986817","display_name":"Yi Cao","orcid":"https://orcid.org/0000-0002-5087-8861"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yi Cao","raw_affiliation_strings":["Management Science and Business Economics Group, University of Edinburgh, UK"],"raw_orcid":"https://orcid.org/0000-0002-5087-8861","affiliations":[{"raw_affiliation_string":"Management Science and Business Economics Group, University of Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"436","last_page":"440"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9998000264167786,"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":0.9998000264167786,"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/T10154","display_name":"Customer Service Quality and Loyalty","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"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/T11536","display_name":"Consumer Retail Behavior Studies","score":0.9836999773979187,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.845272421836853},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7613332271575928},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6245001554489136},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.5909557938575745},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.530238151550293},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4772070348262787},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.4537050127983093},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4516805410385132},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4316543936729431},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.43054643273353577},{"id":"https://openalex.org/keywords/loyalty","display_name":"Loyalty","score":0.4268985092639923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3234304189682007},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.25798314809799194},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.23975953459739685},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14563506841659546}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.845272421836853},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7613332271575928},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6245001554489136},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.5909557938575745},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.530238151550293},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4772070348262787},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.4537050127983093},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4516805410385132},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4316543936729431},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.43054643273353577},{"id":"https://openalex.org/C2776967331","wikidata":"https://www.wikidata.org/wiki/Q1132131","display_name":"Loyalty","level":2,"score":0.4268985092639923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3234304189682007},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.25798314809799194},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.23975953459739685},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14563506841659546},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3569966.3570085","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3569966.3570085","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Computer Science and Software Engineering","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":13,"referenced_works":["https://openalex.org/W1974808478","https://openalex.org/W1979869089","https://openalex.org/W2015557855","https://openalex.org/W2086829791","https://openalex.org/W2088340225","https://openalex.org/W2120887445","https://openalex.org/W2170936641","https://openalex.org/W2345850996","https://openalex.org/W2766555770","https://openalex.org/W2905318875","https://openalex.org/W3154187272","https://openalex.org/W3203644918","https://openalex.org/W4200052650"],"related_works":["https://openalex.org/W3014558862","https://openalex.org/W2356972005","https://openalex.org/W2993362366","https://openalex.org/W3082355735","https://openalex.org/W2377414324","https://openalex.org/W2378879853","https://openalex.org/W4214561858","https://openalex.org/W2373539446","https://openalex.org/W2348701414","https://openalex.org/W2895831816"],"abstract_inverted_index":{"Customer":[0],"segmentation":[1],"is":[2,73,82,101,127,183],"an":[3,98,104],"important":[4,99],"approach":[5],"for":[6],"customer":[7,40,180,187],"relationship":[8],"management,":[9],"in":[10,103,146],"which":[11,57],"many":[12,143],"methods":[13,28,45,145],"are":[14],"achieved":[15],"by":[16],"the":[17,31,50,55,94,112,120,131,136,148,164,175,178,196],"Recency,":[18,32,106,114],"Frequency":[19,33,107,115],"and":[20,23,34,89,108,116,162],"Monetary":[21,35,109,117],"model(RFM)":[22],"clustering":[24,67,91,121,213],"techniques.":[25],"However,":[26],"most":[27,132],"based":[29],"on":[30,174,185],"model":[36,149],"do":[37],"not":[38],"consider":[39],"loyalty.":[41],"In":[42,63,93,119],"addition,":[43],"these":[44,77],"need":[46],"to":[47,75,129,157,168,216],"use":[48],"all":[49],"historical":[51],"data":[52,60,134,170],"when":[53],"updating":[54],"clustering,":[56],"has":[58,210],"high":[59],"storage":[61,171],"requirements.":[62,172],"this":[64],"paper,":[65],"a":[66,70,85,90,123,153,211],"method":[68,81,140,198,209],"with":[69],"time":[71,125,166],"window":[72,126,167],"proposed":[74,80,139,176,197,208],"solve":[76],"problems.":[78],"The":[79,138,191,207],"divided":[83],"into":[84,151,203],"feature":[86,95,155],"selection":[87,96],"stage":[88],"stage.":[92],"stage,":[97,122],"factor":[100],"considered":[102],"improved":[105],"model,":[110],"called":[111],"Length,":[113],"model(LRFM).":[118],"sliding":[124,165],"added":[128],"intercept":[130],"recent":[133],"before":[135],"clustering.":[137],"differs":[141],"from":[142],"other":[144,217],"that":[147,195],"takes":[150],"consideration":[152],"new":[154],"Length":[156],"identify":[158],"customers":[159,202],"more":[160],"accurately,":[161],"uses":[163],"reduce":[169],"Based":[173],"method,":[177],"travel":[179,201],"value":[181],"analysis":[182],"explored":[184],"real":[186],"anonymous":[188],"transaction":[189],"data.":[190],"experimental":[192],"results":[193],"show":[194],"can":[199],"classify":[200],"different":[204],"groups":[205],"effectively.":[206],"better":[212],"performance":[214],"compared":[215],"baseline":[218],"algorithms.":[219]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}