{"id":"https://openalex.org/W2047467486","doi":"https://doi.org/10.1080/10255810210623","title":"A Genetic Algorithm Approach for Semi-Supervised Clustering","display_name":"A Genetic Algorithm Approach for Semi-Supervised Clustering","publication_year":2002,"publication_date":"2002-01-01","ids":{"openalex":"https://openalex.org/W2047467486","doi":"https://doi.org/10.1080/10255810210623","mag":"2047467486"},"language":"en","primary_location":{"id":"doi:10.1080/10255810210623","doi":"https://doi.org/10.1080/10255810210623","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10255810210623","pdf_url":null,"source":{"id":"https://openalex.org/S202006368","display_name":"International Journal of Smart Engineering System Design","issn_l":"1025-5818","issn":["1025-5818","1607-8500"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"International Journal of Smart Engineering System Design","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/A5029134118","display_name":"Ayhan Demiriz","orcid":"https://orcid.org/0000-0002-5731-3134","observed_orcids":["https://orcid.org/0000-0002-5731-3134"]},"institutions":[{"id":"https://openalex.org/I1302485747","display_name":"Verizon (United States)","ror":"https://ror.org/02vdyxx64","country_code":"US","type":"company","lineage":["https://openalex.org/I1302485747"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ayhan Demiriz","raw_affiliation_strings":["E-Business Department, Verizon Inc., Irving, Texas, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"E-Business Department, Verizon Inc., Irving, Texas, USA","institution_ids":["https://openalex.org/I1302485747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048876983","display_name":"Kristin P. Bennett","orcid":"https://orcid.org/0000-0002-8782-105X","observed_orcids":["https://orcid.org/0000-0002-8782-105X"]},"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":"Kristin P. Bennett","raw_affiliation_strings":["Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000204633","display_name":"Mark J. Embrechts","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":"Mark J. Embrechts","raw_affiliation_strings":["Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York, USA","institution_ids":["https://openalex.org/I165799507"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3785,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.84085155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"4","issue":"1","first_page":"21","last_page":"30"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9926999807357788,"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"}},"topics":[{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9926999807357788,"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"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0032999999821186066,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.0008999999845400453,"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/semi-supervised-clustering","display_name":"semi-supervised clustering","score":1.0},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"genetic algorithm","score":0.902999997138977},{"id":"https://openalex.org/keywords/gini-coefficient","display_name":"Gini coefficient","score":0.8870000243186951},{"id":"https://openalex.org/keywords/capacity-control","display_name":"capacity control","score":0.6869999766349792},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.8339999914169312},{"id":"https://openalex.org/keywords/unlabeled-data","display_name":"unlabeled data","score":0.7710000276565552},{"id":"https://openalex.org/keywords/customer-segmentation","display_name":"customer segmentation","score":0.6970000267028809},{"id":"https://openalex.org/keywords/database-marketing","display_name":"database marketing","score":0.8960000276565552},{"id":"https://openalex.org/keywords/classification-accuracy","display_name":"classification accuracy","score":0.4429999887943268}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7044858336448669},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5817577242851257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4928905963897705},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4491807520389557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40066301822662354},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3507981300354004}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10255810210623","doi":"https://doi.org/10.1080/10255810210623","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10255810210623","pdf_url":null,"source":{"id":"https://openalex.org/S202006368","display_name":"International Journal of Smart Engineering System Design","issn_l":"1025-5818","issn":["1025-5818","1607-8500"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"International Journal of Smart Engineering System Design","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.105.5073","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.5073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://web.sakarya.edu.tr/~ademiriz/papers/ssclus.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1594031697","https://openalex.org/W1639032689","https://openalex.org/W1874786585","https://openalex.org/W1967550647","https://openalex.org/W1971784203","https://openalex.org/W1987865517","https://openalex.org/W2028944928","https://openalex.org/W2041794780","https://openalex.org/W2044304288","https://openalex.org/W2047019718","https://openalex.org/W2051224630","https://openalex.org/W2099189653","https://openalex.org/W2107008379","https://openalex.org/W2107968230","https://openalex.org/W2136137122","https://openalex.org/W2148603752","https://openalex.org/W2149706766","https://openalex.org/W2319660501","https://openalex.org/W2330820318","https://openalex.org/W2941515383","https://openalex.org/W3023540311","https://openalex.org/W3085162807","https://openalex.org/W3112020351"],"related_works":["https://openalex.org/W7095945794","https://openalex.org/W7097639068","https://openalex.org/W3147078940","https://openalex.org/W2356012993","https://openalex.org/W1961784407","https://openalex.org/W2903724903","https://openalex.org/W2793437477","https://openalex.org/W2767072179","https://openalex.org/W2536035781","https://openalex.org/W2952733303"],"abstract_inverted_index":{"A":[0],"novel":[1],"semi-supervised":[2],"clustering":[3,106],"algorithm":[4,176],"is":[5,84,114,144,177],"proposed":[6,178],"that":[7,85,199,222,246],"synergizes":[8],"the":[9,46,67,111,119,123,134,138,142,154,161,193,205,215,223],"benefits":[10],"of":[11,35,48,66,81,103,122,129,141,147],"supervised":[12],"and":[13,63,126],"unsupervised":[14,22,210],"learning":[15,23],"methods.":[16],"Data":[17],"are":[18,190,233],"clustered":[19],"using":[20,200,236],"an":[21,182],"technique":[24,57],"biased":[25],"toward":[26],"producing":[27],"clusters":[28,39,189],"as":[29,31,160,248],"pure":[30],"possible":[32],"in":[33,53,110],"terms":[34],"class":[36,47,92,135,201],"distribution.":[37],"These":[38],"can":[40,58,164,241],"then":[41],"be":[42,59,94,166],"used":[43,60,95],"to":[44,61,71,73,93,96,116,150,168,179,185,209],"predict":[45],"future":[49],"points.":[50],"For":[51,153],"example,":[52],"database":[54],"marketing":[55],"this":[56,82,169,172],"identify":[62],"characterize":[64],"segments":[65],"customer":[68],"population":[69],"likely":[70],"respond":[72],"a":[74,104,127,145,174],"specific":[75],"promotion.":[76],"One":[77],"key":[78],"additional":[79],"benefit":[80],"approach":[83],"it":[86],"allows":[87],"unlabeled":[88,239],"data":[89,240,247],"with":[90,192],"unknown":[91],"improve":[97,242],"classification":[98,243],"accuracy.":[99],"The":[100],"objective":[101,183],"function":[102,184],"traditional":[105],"technique,":[107],"cluster":[108,130],"dispersion":[109],"K-means":[112],"algorithm,":[113],"modified":[115],"minimize":[117],"both":[118],"within-cluster":[120,139],"variance":[121,140],"input":[124,216],"attributes":[125],"measure":[128],"impurity":[131,157],"based":[132,212],"on":[133,214,245],"labels.":[136],"Minimizing":[137],"examples":[143,232],"form":[146],"capacity":[148],"control":[149],"prevent":[151],"overfitting.":[152],"output":[155],"labels,":[156],"measures":[158],"such":[159,181],"Gini":[162],"index":[163],"readily":[165],"applied":[167],"problem.":[170],"In":[171],"work,":[173],"genetic":[175],"optimize":[180],"produce":[186],"clusters.":[187],"Non-empty":[188],"labeled":[191],"majority":[194],"class.":[195],"Experimental":[196],"results":[197],"show":[198],"information":[202,237],"often":[203],"improves":[204],"generalization":[206],"ability":[207],"compared":[208],"methods":[211],"only":[213],"attributes.":[217],"Benchmark":[218],"studies":[219],"also":[220],"indicate":[221],"method":[224],"performs":[225],"very":[226],"well":[227],"even":[228],"when":[229],"few":[230],"training":[231],"available.":[234],"Training":[235],"from":[238],"accuracy":[244],"well.":[249]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2016-06-24T00:00:00"}
