{"id":"https://openalex.org/W2026705124","doi":"https://doi.org/10.1145/2020408.2020516","title":"Clustering very large multi-dimensional datasets with MapReduce","display_name":"Clustering very large multi-dimensional datasets with MapReduce","publication_year":2011,"publication_date":"2011-08-21","ids":{"openalex":"https://openalex.org/W2026705124","doi":"https://doi.org/10.1145/2020408.2020516","mag":"2026705124"},"language":"en","primary_location":{"id":"doi:10.1145/2020408.2020516","doi":"https://doi.org/10.1145/2020408.2020516","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2020408.2020516","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5085964688","display_name":"Robson L. F. Cordeiro","orcid":"https://orcid.org/0000-0002-6795-3004","observed_orcids":["https://orcid.org/0000-0002-6795-3004"]},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Robson Leonardo Ferreira Cordeiro","raw_affiliation_strings":["University of S\u00e3o Paulo, S\u00e3o Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of S\u00e3o Paulo, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020611078","display_name":"Caetano Traina","orcid":"https://orcid.org/0000-0002-6625-6047","observed_orcids":["https://orcid.org/0000-0002-6625-6047"]},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Caetano Traina","raw_affiliation_strings":["University of S\u00e3o Paulo, S\u00e3o Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of S\u00e3o Paulo, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014783020","display_name":"Agma J. M. Traina","orcid":"https://orcid.org/0000-0003-4929-7258","observed_orcids":["https://orcid.org/0000-0003-4929-7258"]},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Agma Juci Machado Traina","raw_affiliation_strings":["University of S\u00e3o Paulo, S\u00e3o Carlos, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of S\u00e3o Paulo, S\u00e3o Carlos, Brazil","institution_ids":["https://openalex.org/I17974374"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Julio L\u00f3pez","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Julio L\u00f3pez","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074561505","display_name":"U Kang","orcid":"https://orcid.org/0000-0002-8774-6950","observed_orcids":["https://orcid.org/0000-0002-8774-6950"]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"U. Kang","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035605036","display_name":"Christos Faloutsos","orcid":"https://orcid.org/0000-0003-2996-9790","observed_orcids":["https://orcid.org/0000-0003-2996-9790"]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christos Faloutsos","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA","Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, Pa., USA#TAB#","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":16.8414,"has_fulltext":false,"cited_by_count":148,"citation_normalized_percentile":{"value":0.99599848,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"690","last_page":"698"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.5288000106811523,"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":0.5288000106811523,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.34130001068115234,"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.029600000008940697,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/clustering","display_name":"clustering","score":0.4399999976158142},{"id":"https://openalex.org/keywords/mapreduce","display_name":"MapReduce","score":0.9629999995231628},{"id":"https://openalex.org/keywords/high-dimensional-data","display_name":"high-dimensional data","score":0.7950000166893005},{"id":"https://openalex.org/keywords/very-large-data-sets","display_name":"very large datasets","score":0.7870000004768372},{"id":"https://openalex.org/keywords/parallel-clustering","display_name":"parallel clustering","score":0.6620000004768372},{"id":"https://openalex.org/keywords/i-o-cost","display_name":"I/O cost","score":0.9150000214576721},{"id":"https://openalex.org/keywords/network-cost","display_name":"network cost","score":0.9710000157356262},{"id":"https://openalex.org/keywords/disk-access","display_name":"disk access","score":0.5699999928474426},{"id":"https://openalex.org/keywords/cost-trade-offs","display_name":"cost trade-offs","score":0.4699999988079071},{"id":"https://openalex.org/keywords/subspace-clustering","display_name":"subspace clustering","score":0.6570000052452087},{"id":"https://openalex.org/keywords/web-data","display_name":"web data","score":0.3580000102519989}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8449783325195312},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8128517866134644},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6807022094726562},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.5990889072418213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5564567446708679},{"id":"https://openalex.org/C96147967","wikidata":"https://www.wikidata.org/wiki/Q190686","display_name":"Subroutine","level":2,"score":0.5474721193313599},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.4899227023124695},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.42819347977638245},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4132521450519562},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.3852190673351288},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.371156245470047},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3429015278816223},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27419304847717285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18046391010284424},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2020408.2020516","doi":"https://doi.org/10.1145/2020408.2020516","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2020408.2020516","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.417.5086","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.417.5086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~christos/PUBLICATIONS/kdd2011_clustering_MR.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W91199005","https://openalex.org/W1967838552","https://openalex.org/W1977496278","https://openalex.org/W1997728726","https://openalex.org/W2065811242","https://openalex.org/W2067877017","https://openalex.org/W2073825221","https://openalex.org/W2079361215","https://openalex.org/W2089055054","https://openalex.org/W2093100781","https://openalex.org/W2103696938","https://openalex.org/W2109130989","https://openalex.org/W2118858274","https://openalex.org/W2126642639","https://openalex.org/W2161284614","https://openalex.org/W2167927436","https://openalex.org/W2168143534","https://openalex.org/W2173213060","https://openalex.org/W3003734944"],"related_works":["https://openalex.org/W7098506038","https://openalex.org/W4254487859","https://openalex.org/W2061051181","https://openalex.org/W2116762767","https://openalex.org/W2055903840","https://openalex.org/W1575564412","https://openalex.org/W2109380209","https://openalex.org/W1977552872","https://openalex.org/W3080538929","https://openalex.org/W2576259462"],"abstract_inverted_index":{"Given":[0],"a":[1,20,25,79,99,140,155],"very":[2,156],"large":[3],"moderate-to-high":[4],"dimensionality":[5],"dataset,":[6],"how":[7,46,65],"could":[8],"one":[9],"cluster":[10],"its":[11,113],"points?":[12],"For":[13],"datasets":[14],"that":[15,92,125],"don't":[16],"fit":[17],"even":[18],"on":[19,61,192],"single":[21],"disk,":[22],"parallelism":[23],"is":[24,217],"first":[26],"class":[27],"option.":[28],"In":[29],"this":[30,37],"paper":[31],"we":[32,82],"explore":[33],"MapReduce":[34],"for":[35,148],"clustering":[36,137,142,175,228],"kind":[38],"of":[39,75,86,177,199,211,233],"data.":[40],"The":[41],"main":[42,103],"questions":[43],"are":[44],"(a)":[45],"to":[47,66,168,203,241],"minimize":[48,67],"the":[49,55,68,84,95,119,146,160,174,178,209,218,225],"I/O":[50],"cost,":[51],"taking":[52],"into":[53],"account":[54],"already":[56],"existing":[57],"data":[58,196],"partition":[59],"(e.g.,":[60],"disks),":[62],"and":[63,97,110,151,181,186,194],"(b)":[64],"network":[69,152],"cost":[70,114,147],"among":[71],"processing":[72],"nodes.":[73],"Either":[74],"them":[76],"may":[77],"be":[78,242],"bottleneck.":[80],"Thus,":[81],"propose":[83,108],"Best":[85],"both":[87],"Worlds":[88],"--":[89],"BoW":[90,109,126],"method,":[91],"automatically":[93],"spots":[94],"bottleneck":[96],"chooses":[98],"good":[100,157],"strategy.":[101],"Our":[102],"contributions":[104],"are:":[105],"(1)":[106],"We":[107,123,189],"carefully":[111],"derive":[112],"functions,":[115],"which":[116],"dynamically":[117],"choose":[118],"best":[120,210],"strategy;":[121],"(2)":[122],"show":[124],"has":[127,183],"numerous":[128],"desirable":[129],"features:":[130],"it":[131,144,162,172,182,236],"can":[132],"work":[133],"with":[134,197],"most":[135],"serial":[136,179],"methods":[138],"as":[139],"plugged-in":[141],"subroutine,":[143],"balances":[145],"disk":[149],"accesses":[150],"accesses,":[153],"achieving":[154],"tradeoff":[158],"between":[159],"two,":[161],"uses":[163],"no":[164],"user-defined":[165],"parameters":[166],"(thanks":[167],"our":[169,212,214],"reasonable":[170],"defaults),":[171],"matches":[173],"quality":[176],"algorithm,":[180],"near-linear":[184],"scale-up;":[185],"finally,":[187],"(3)":[188],"report":[190],"experiments":[191],"real":[193,220],"synthetic":[195],"billions":[198],"points,":[200],"using":[201,244],"up":[202],"1,024":[204],"cores":[205],"in":[206,224],"parallel.":[207],"To":[208],"knowledge,":[213],"Yahoo!":[215],"web":[216],"largest":[219],"dataset":[221],"ever":[222],"reported":[223],"database":[226],"subspace":[227],"literature.":[229],"Spanning":[230],"0.2":[231],"TB":[232],"multi-dimensional":[234],"data,":[235],"took":[237],"only":[238],"8":[239],"minutes":[240],"clustered,":[243],"128":[245],"cores.":[246]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":15},{"year":2016,"cited_by_count":22},{"year":2015,"cited_by_count":20},{"year":2014,"cited_by_count":29},{"year":2013,"cited_by_count":18},{"year":2012,"cited_by_count":14}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
