{"id":"https://openalex.org/W3126928232","doi":"https://doi.org/10.1109/icdm50108.2020.00009","title":"Approximation Algorithms for Probabilistic k-Center Clustering","display_name":"Approximation Algorithms for Probabilistic k-Center Clustering","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3126928232","doi":"https://doi.org/10.1109/icdm50108.2020.00009","mag":"3126928232"},"language":"en","primary_location":{"id":"doi:10.1109/icdm50108.2020.00009","doi":"https://doi.org/10.1109/icdm50108.2020.00009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm50108.2020.00009","pdf_url":null,"source":{"id":"https://openalex.org/S4210214883","display_name":"Proceedings","issn_l":"1550-4786","issn":["1550-4786","2374-8486"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Data Mining (ICDM)","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/A5041521633","display_name":"Sharareh Alipour","orcid":"https://orcid.org/0000-0002-3626-8960","observed_orcids":["https://orcid.org/0000-0002-3626-8960"]},"institutions":[{"id":"https://openalex.org/I4210146419","display_name":"Institute for Research in Fundamental Sciences","ror":"https://ror.org/04xreqs31","country_code":"IR","type":"facility","lineage":["https://openalex.org/I4210146419"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Sharareh Alipour","raw_affiliation_strings":["School of computer science Institute for research in fundamental sciences, IPM, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of computer science Institute for research in fundamental sciences, IPM, Tehran, Iran","institution_ids":["https://openalex.org/I4210146419"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5041521633"],"corresponding_institution_ids":["https://openalex.org/I4210146419"],"apc_list":null,"apc_paid":null,"fwci":0.398,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.59612653,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.365200012922287,"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":0.365200012922287,"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/T10996","display_name":"Computational Geometry and Mesh Generation","score":0.2957000136375427,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.28200000524520874,"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/approximation-algorithms","display_name":"approximation algorithms","score":0.7039999961853027},{"id":"https://openalex.org/keywords/data-uncertainty","display_name":"data uncertainty","score":0.8040000200271606},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"greedy algorithm","score":0.7049999833106995},{"id":"https://openalex.org/keywords/approximation-ratio","display_name":"approximation ratio","score":0.6439999938011169},{"id":"https://openalex.org/keywords/metric-spaces","display_name":"metric spaces","score":0.550000011920929}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6441203951835632},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6430471539497375},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6258528828620911},{"id":"https://openalex.org/C2779463800","wikidata":"https://www.wikidata.org/wiki/Q5062222","display_name":"Center (category theory)","level":2,"score":0.5252085328102112},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4446130394935608},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33880653977394104},{"id":"https://openalex.org/C8010536","wikidata":"https://www.wikidata.org/wiki/Q160398","display_name":"Crystallography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm50108.2020.00009","doi":"https://doi.org/10.1109/icdm50108.2020.00009","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm50108.2020.00009","pdf_url":null,"source":{"id":"https://openalex.org/S4210214883","display_name":"Proceedings","issn_l":"1550-4786","issn":["1550-4786","2374-8486"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1482186473","https://openalex.org/W1505129060","https://openalex.org/W1521571351","https://openalex.org/W1529668588","https://openalex.org/W1628490320","https://openalex.org/W1673310716","https://openalex.org/W1969642980","https://openalex.org/W1973264045","https://openalex.org/W1998932824","https://openalex.org/W2003207175","https://openalex.org/W2032624334","https://openalex.org/W2040783379","https://openalex.org/W2045964207","https://openalex.org/W2050632537","https://openalex.org/W2059651397","https://openalex.org/W2099878960","https://openalex.org/W2107423006","https://openalex.org/W2110553125","https://openalex.org/W2114353529","https://openalex.org/W2133311553","https://openalex.org/W2133962824","https://openalex.org/W2134746140","https://openalex.org/W2137536943","https://openalex.org/W2139594840","https://openalex.org/W2141178123","https://openalex.org/W2157754768","https://openalex.org/W2753741016","https://openalex.org/W2950813123","https://openalex.org/W3137423196","https://openalex.org/W4254220436"],"related_works":["https://openalex.org/W3196820231","https://openalex.org/W2891859780","https://openalex.org/W2753741016","https://openalex.org/W4400984916","https://openalex.org/W40987029","https://openalex.org/W3215036344","https://openalex.org/W7118526006","https://openalex.org/W4386035023","https://openalex.org/W7096020004","https://openalex.org/W7097242791"],"abstract_inverted_index":{"Uncertainty":[0],"about":[1,241],"data":[2,28,34,203,225],"appears":[3],"in":[4,73,94,187,210,221],"many":[5],"realworld":[6],"applications":[7],"and":[8,17,133,239],"an":[9,137,150],"important":[10,25],"issue":[11],"is":[12,30,36,144,192],"how":[13],"to":[14,114,121,176],"manage,":[15],"analyze":[16],"solve":[18],"optimization":[19],"problems":[20],"over":[21],"such":[22],"data.":[23],"An":[24],"tool":[26],"for":[27,70,140,149,223],"analysis":[29],"clustering.":[31],"When":[32],"the":[33,57,61,100,105,119,126,130,154,167,171,180,212,230,236,242],"set":[35,44],"uncertain,":[37],"we":[38,66,79,117,134,165,196,228],"can":[39],"model":[40],"them":[41],"as":[42,50],"a":[43,51,74,81,91,145,193],"of":[45,60,104,129,153,170,215,232],"probabilistic":[46,71,127],"points":[47,72,132],"each":[48],"formalized":[49],"probability":[52],"distribution":[53],"function":[54],"which":[55],"describes":[56],"possible":[58],"locations":[59,128],"points.":[62],"In":[63],"this":[64,141,163],"paper,":[65],"study":[67],"k-center":[68,108,156,174],"problem":[69,109,157,175],"general":[75],"metric":[76],"space.":[77],"First":[78],"present":[80],"fast":[82],"greedy":[83],"approximation":[84,102,168,213],"algorithm":[85,98,184,234],"that":[86,136,209],"builds":[87],"k":[88,95,191],"centers":[89,120],"using":[90],"farthest-first":[92],"traversal":[93],"iterations.":[96],"This":[97],"improves":[99],"previous":[101,237],"factor":[103,147,169],"unrestricted":[106,172],"assigned":[107,155,173],"from":[110,124],"10":[111],"(see":[112],"[1])":[113],"6.":[115],"Next":[116],"restrict":[118],"be":[122],"selected":[123],"all":[125],"given":[131],"show":[135,208],"optimal":[138,151],"solution":[139,148,152],"restricted":[142],"setting":[143],"2-approximation":[146],"with":[158,235],"expected":[159],"distance":[160],"assignment.":[161],"Using":[162],"idea,":[164],"improve":[166],"4":[177],"by":[178],"increasing":[179],"running":[181],"time.":[182],"The":[183,205],"also":[185],"runs":[186],"polynomial":[188],"time":[189],"when":[190],"constant.":[194],"Additionally,":[195],"implement":[197],"our":[198,216,233],"algorithms":[199,217],"on":[200],"three":[201],"real":[202],"sets.":[204,226],"experimental":[206],"results":[207,231],"practice":[211],"factors":[214],"are":[218],"better":[219],"than":[220],"theory":[222],"these":[224],"Also":[227],"compare":[229],"works":[238],"discuss":[240],"achieved":[243],"results.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-02-15T00:00:00"}