{"id":"https://openalex.org/W2167187892","doi":"https://doi.org/10.1109/icdmw.2007.45","title":"Multiple-Criteria Linear Programming for VIP E-Mail Behavior Analysis","display_name":"Multiple-Criteria Linear Programming for VIP E-Mail Behavior Analysis","publication_year":2007,"publication_date":"2007-10-01","ids":{"openalex":"https://openalex.org/W2167187892","doi":"https://doi.org/10.1109/icdmw.2007.45","mag":"2167187892"},"language":"en","primary_location":{"id":"doi:10.1109/icdmw.2007.45","doi":"https://doi.org/10.1109/icdmw.2007.45","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw.2007.45","pdf_url":null,"source":{"id":"https://openalex.org/S4393918563","display_name":"IEEE ... International Conference on Data Mining workshops","issn_l":"2375-9259","issn":["2375-9259"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5100364041","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0001-7973-2746","observed_orcids":["https://orcid.org/0000-0001-7973-2746","https://orcid.org/0000-0002-0994-2382","https://orcid.org/0000-0001-9518-5914","https://orcid.org/0000-0001-5656-1083","https://orcid.org/0000-0001-5352-9395"]},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["School of Information Science and Engineering, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053184874","display_name":"Jingran Dai","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingran Dai","raw_affiliation_strings":["Rensselaer Polytechnic Institute, Troy, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rensselaer Polytechnic Institute, Troy, NY, USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":19.0625,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.99179655,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"289","last_page":"296"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9878000020980835,"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":0.9878000020980835,"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":0.004999999888241291,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.000699999975040555,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"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/multiple-criteria-linear-programming","display_name":"multiple criteria linear programming","score":0.9179999828338623},{"id":"https://openalex.org/keywords/customer-churn-prediction","display_name":"customer churn prediction","score":0.7120000123977661},{"id":"https://openalex.org/keywords/customer-retention","display_name":"customer retention","score":0.6959999799728394},{"id":"https://openalex.org/keywords/ensemble-methods","display_name":"ensemble methods","score":0.4860000014305115},{"id":"https://openalex.org/keywords/cross-validation","display_name":"cross-validation","score":0.6150000095367432}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6543188095092773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5128774642944336},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48780420422554016},{"id":"https://openalex.org/C3020028006","wikidata":"https://www.wikidata.org/wiki/Q9158","display_name":"Electronic mail","level":2,"score":0.48132097721099854},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.4166181683540344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3657749891281128},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10323387384414673},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0815819501876831}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw.2007.45","doi":"https://doi.org/10.1109/icdmw.2007.45","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw.2007.45","pdf_url":null,"source":{"id":"https://openalex.org/S4393918563","display_name":"IEEE ... International Conference on Data Mining workshops","issn_l":"2375-9259","issn":["2375-9259"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1485585868","https://openalex.org/W1500895378","https://openalex.org/W1542796676","https://openalex.org/W1549656520","https://openalex.org/W1552810732","https://openalex.org/W1555428313","https://openalex.org/W1560724230","https://openalex.org/W1589603734","https://openalex.org/W1607723598","https://openalex.org/W1749992802","https://openalex.org/W1828132036","https://openalex.org/W1956647075","https://openalex.org/W2064123298","https://openalex.org/W2097839764","https://openalex.org/W2115229333","https://openalex.org/W2115519811","https://openalex.org/W2135346934","https://openalex.org/W2138376168","https://openalex.org/W2140190241","https://openalex.org/W2143426320","https://openalex.org/W2153631847","https://openalex.org/W2156909104","https://openalex.org/W2310653300","https://openalex.org/W3120740533","https://openalex.org/W4237171445","https://openalex.org/W4285719527","https://openalex.org/W6680704940"],"related_works":["https://openalex.org/W1595133258","https://openalex.org/W1552810732","https://openalex.org/W2103927283","https://openalex.org/W1555428313","https://openalex.org/W239844438","https://openalex.org/W1559935822","https://openalex.org/W1495237869","https://openalex.org/W2002202987","https://openalex.org/W71580036","https://openalex.org/W2025802222"],"abstract_inverted_index":{"Excessive":[0],"lose":[1],"of":[2,16,18,59,77,97,120],"customer":[3],"account":[4],"is":[5,62,103],"becoming":[6],"a":[7,38],"major":[8],"headache":[9],"for":[10,34],"VIP":[11,68,78,109,123],"E-Mail":[12,69,79,110,124],"hosting":[13],"companies.":[14,36],"Analysis":[15],"features":[17,76],"easily":[19],"lost":[20],"customers":[21],"and":[22,93,106],"finding":[23],"the":[24,75,82,98,118],"appropriate":[25],"measures":[26],"to":[27,52,63,67,81],"sustain":[28],"them":[29],"has":[30,48],"become":[31],"pressing":[32],"needs":[33],"these":[35],"As":[37],"promising":[39],"data":[40],"mining":[41],"approach,":[42],"Multiple":[43],"Criteria":[44],"Linear":[45],"Programming":[46],"(MCLP)":[47],"been":[49],"successfully":[50],"applied":[51],"classify":[53],"credit":[54],"cardholders'":[55],"behavior.":[56],"The":[57,95],"purpose":[58],"this":[60],"paper":[61],"apply":[64],"MCLP":[65,89,102,121],"model":[66],"behavior":[70,125],"classification;":[71],"we":[72,87],"first":[73],"extracted":[74],"according":[80],"accumulated":[83],"experience":[84],"functions,":[85],"then":[86],"tested":[88],"with":[90],"cross-":[91],"validation":[92],"ensemble.":[94],"results":[96],"study":[99],"showed":[100],"that":[101,117],"sufficiently":[104],"accurate":[105],"stable":[107],"on":[108,122],"dataset.":[111],"Therefore":[112],"it":[113],"can":[114,127],"be":[115],"concluded":[116],"application":[119],"analysis":[126],"provide":[128],"credible":[129],"results.":[130]},"counts_by_year":[{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":6}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2016-06-24T00:00:00"}
