{"id":"https://openalex.org/W4414688402","doi":"https://doi.org/10.1145/3770078","title":"Towards Fair Decision Boundaries in Clustering: Integrating Disparate Impact Criteria into Maximum Margin Clustering","display_name":"Towards Fair Decision Boundaries in Clustering: Integrating Disparate Impact Criteria into Maximum Margin Clustering","publication_year":2025,"publication_date":"2025-10-01","ids":{"openalex":"https://openalex.org/W4414688402","doi":"https://doi.org/10.1145/3770078"},"language":"en","primary_location":{"id":"doi:10.1145/3770078","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3770078","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-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/A5055768801","display_name":"Adithya K Moorthy","orcid":"https://orcid.org/0009-0004-2871-8548"},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Adithya K. Moorthy","raw_affiliation_strings":["Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India","Indian Institute of Technology, Guwahati, India"],"raw_orcid":"https://orcid.org/0009-0004-2871-8548","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India","institution_ids":["https://openalex.org/I1317621060"]},{"raw_affiliation_string":"Indian Institute of Technology, Guwahati, India","institution_ids":["https://openalex.org/I1317621060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119790839","display_name":"Jaya Teja Reddy Pochimireddy","orcid":null},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jaya Teja Reddy Pochimireddy","raw_affiliation_strings":["Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India","Indian Institute of Technology, Guwahati, India"],"raw_orcid":"https://orcid.org/0009-0003-5003-3716","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India","institution_ids":["https://openalex.org/I1317621060"]},{"raw_affiliation_string":"Indian Institute of Technology, Guwahati, India","institution_ids":["https://openalex.org/I1317621060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043100023","display_name":"Vijaya Saradhi Vedula","orcid":"https://orcid.org/0000-0002-7856-5322"},"institutions":[{"id":"https://openalex.org/I1317621060","display_name":"Indian Institute of Technology Guwahati","ror":"https://ror.org/0022nd079","country_code":"IN","type":"education","lineage":["https://openalex.org/I1317621060"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vijaya Saradhi Vedula","raw_affiliation_strings":["Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India","Indian Institute of Technology, Guwahati, India"],"raw_orcid":"https://orcid.org/0000-0002-7856-5322","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, India","institution_ids":["https://openalex.org/I1317621060"]},{"raw_affiliation_string":"Indian Institute of Technology, Guwahati, India","institution_ids":["https://openalex.org/I1317621060"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108033355","display_name":"Bhanu Prasad","orcid":"https://orcid.org/0000-0002-3585-655X"},"institutions":[{"id":"https://openalex.org/I8248082","display_name":"Florida Agricultural and Mechanical University","ror":"https://ror.org/00c4wc133","country_code":"US","type":"education","lineage":["https://openalex.org/I8248082"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhanu Prasad","raw_affiliation_strings":["Department of Computer and Information Sciences, Florida Agricultural and Mechanical University, Tallahassee, Florida, USA","Florida Agricultural and Mechanical University, USA"],"raw_orcid":"https://orcid.org/0000-0002-3585-655X","affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Florida Agricultural and Mechanical University, Tallahassee, Florida, USA","institution_ids":["https://openalex.org/I8248082"]},{"raw_affiliation_string":"Florida Agricultural and Mechanical University, USA","institution_ids":["https://openalex.org/I8248082"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23836859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":"9","first_page":"1","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9508000016212463,"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/cluster-analysis","display_name":"Cluster analysis","score":0.886900007724762},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6495000123977661},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.5515000224113464},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.536300003528595},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5090000033378601},{"id":"https://openalex.org/keywords/decision-boundary","display_name":"Decision boundary","score":0.4984999895095825},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4781000018119812},{"id":"https://openalex.org/keywords/disparate-impact","display_name":"Disparate impact","score":0.460999995470047},{"id":"https://openalex.org/keywords/hyperplane","display_name":"Hyperplane","score":0.43779999017715454}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.886900007724762},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6495000123977661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6248999834060669},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.5515000224113464},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.536300003528595},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5318999886512756},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5133000016212463},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5090000033378601},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.4984999895095825},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4781000018119812},{"id":"https://openalex.org/C2776889015","wikidata":"https://www.wikidata.org/wiki/Q5282532","display_name":"Disparate impact","level":3,"score":0.460999995470047},{"id":"https://openalex.org/C68693459","wikidata":"https://www.wikidata.org/wiki/Q657586","display_name":"Hyperplane","level":2,"score":0.43779999017715454},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4230000078678131},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.3869999945163727},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.35830000042915344},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.3310999870300293},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.2976999878883362},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.29249998927116394},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.2888999879360199},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3770078","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3770078","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1583837637","https://openalex.org/W1819662813","https://openalex.org/W1961345416","https://openalex.org/W1987971958","https://openalex.org/W2014352947","https://openalex.org/W2115245664","https://openalex.org/W2187089797","https://openalex.org/W2584805976","https://openalex.org/W2613903811","https://openalex.org/W2788241675","https://openalex.org/W2947657760","https://openalex.org/W3033293774","https://openalex.org/W3096290781","https://openalex.org/W3134473896","https://openalex.org/W3135038576","https://openalex.org/W3195765557","https://openalex.org/W3200455951","https://openalex.org/W4239110337","https://openalex.org/W4282813715","https://openalex.org/W4362589150","https://openalex.org/W4386363051","https://openalex.org/W4388229648","https://openalex.org/W4390003022","https://openalex.org/W4390954627","https://openalex.org/W4400524870","https://openalex.org/W4402350678","https://openalex.org/W4404279187","https://openalex.org/W4408276406","https://openalex.org/W6888840370","https://openalex.org/W6888931462","https://openalex.org/W6907693833","https://openalex.org/W6945341957"],"related_works":[],"abstract_inverted_index":{"Extensive":[0],"application":[1],"of":[2,34,40,80,86,123,160,168,173,195,204],"machine":[3],"learning":[4,55],"in":[5,17,26,73,89,171],"the":[6,32,38,81,84,90,105,110,121,152,158,161,184,192,196,202],"areas":[7],"that":[8,20,120,132,157,167,183],"impact":[9,51,107],"human":[10],"lives":[11],"has":[12],"significantly":[13],"spurred":[14],"considerable":[15],"interest":[16],"developing":[18],"algorithms":[19,36],"are":[21],"demonstrably":[22],"fair.":[23],"Recent":[24],"efforts":[25],"this":[27,46],"field":[28],"have":[29],"led":[30],"to":[31,142],"creation":[33],"numerous":[35],"addressing":[37],"paradigm":[39],"clustering":[41,61,74,189,193],"with":[42,78,131],"fairness":[43,71,175],"constraints.":[44],"In":[45],"research,":[47],"we":[48],"adopt":[49],"disparate":[50,106],"criteria":[52,108],"from":[53,127,147],"supervised":[54],"scenarios":[56,75],"and":[57,211],"incorporate":[58],"it":[59],"into":[60,109],"by":[62,103,151],"specifically":[63],"focusing":[64],"on":[65,209],"decision":[66,153],"boundary":[67],"fairness.":[68],"The":[69,116],"existing":[70],"definitions":[72],"mostly":[76],"deal":[77],"Balance":[79],"clusters":[82],"or":[83],"representation":[85],"sensitive":[87,135,145],"groups":[88],"clusters.":[91],"We":[92,155,180,200],"developed":[93],"a":[94],"new":[95],"algorithm":[96,118,163,170,186],"called":[97],"Fair":[98],"Maximum":[99,111],"Margin":[100,112],"Clustering":[101,113],"(FMMC),":[102],"incorporating":[104],"(MMC)":[114],"algorithm.":[115,199],"FMMC":[117,162,185],"ensures":[119],"distance":[122],"each":[124],"data":[125,133],"point":[126],"hyperplane":[128],"is":[129,140,164],"uncorrelated":[130],"point\u2019s":[134],"attribute":[136],"value.":[137],"This":[138],"constraint":[139],"designed":[141],"prevent":[143],"any":[144],"group":[146],"being":[148],"negatively":[149],"impacted":[150],"boundary.":[154],"show":[156],"performance":[159,194],"better":[165],"than":[166],"MMC":[169,198],"terms":[172],"traditional":[174],"measures":[176],"such":[177],"as":[178],"Balance.":[179],"also":[181],"demonstrate":[182],"achieves":[187],"fair":[188],"while":[190],"maintaining":[191],"original":[197],"validate":[201],"effectiveness":[203],"our":[205],"approach":[206],"through":[207],"experiments":[208],"synthetic":[210],"real-world":[212],"datasets.":[213]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}