{"id":"https://openalex.org/W2038228075","doi":"https://doi.org/10.1145/1014052.1014131","title":"The IOC algorithm","display_name":"The IOC algorithm","publication_year":2004,"publication_date":"2004-08-22","ids":{"openalex":"https://openalex.org/W2038228075","doi":"https://doi.org/10.1145/1014052.1014131","mag":"2038228075"},"language":"en","primary_location":{"id":"doi:10.1145/1014052.1014131","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1014131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-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/A5101790772","display_name":"Ting Liu","orcid":"https://orcid.org/0000-0001-9520-5081"},"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":true,"raw_author_name":"Ting Liu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA","Carnegie-Mellon University, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031932067","display_name":"Ke Yang","orcid":"https://orcid.org/0000-0002-6441-2309"},"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":"Ke Yang","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA","Carnegie-Mellon University, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101927521","display_name":"Andrew Moore","orcid":"https://orcid.org/0000-0002-3395-0841"},"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":"Andrew W. Moore","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA","Carnegie-Mellon University, Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie-Mellon University, Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101790772"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":1.1217,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.77693884,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"629","last_page":"634"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11269","display_name":"Algorithms and Data Compression","score":0.9952999949455261,"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/computer-science","display_name":"Computer science","score":0.7063445448875427},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.6636144518852234},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.661695122718811},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5729804039001465},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5605129599571228},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5584918260574341},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5552008748054504},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5538922548294067},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5290621519088745},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47517839074134827},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46229618787765503},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.41280683875083923},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.41223135590553284},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3899378180503845},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36663734912872314},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16941514611244202}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7063445448875427},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.6636144518852234},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.661695122718811},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5729804039001465},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5605129599571228},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5584918260574341},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5552008748054504},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5538922548294067},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5290621519088745},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47517839074134827},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46229618787765503},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.41280683875083923},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.41223135590553284},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3899378180503845},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36663734912872314},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16941514611244202},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1014052.1014131","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1014131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W117742025","https://openalex.org/W1502916507","https://openalex.org/W1541459201","https://openalex.org/W1571061365","https://openalex.org/W2024668293","https://openalex.org/W2033253727","https://openalex.org/W2049644877","https://openalex.org/W2124741252","https://openalex.org/W2134553237","https://openalex.org/W2138064700","https://openalex.org/W2145725688","https://openalex.org/W2147169507","https://openalex.org/W2153353990","https://openalex.org/W2157092487","https://openalex.org/W2427881153","https://openalex.org/W3041834803","https://openalex.org/W6629956336"],"related_works":["https://openalex.org/W4389954502","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W4385770464","https://openalex.org/W4224262160","https://openalex.org/W2153677915","https://openalex.org/W3120363735","https://openalex.org/W2394323384","https://openalex.org/W4387382336"],"abstract_inverted_index":{"This":[0,105,175],"paper":[1,69,135],"is":[2,193],"about":[3],"a":[4,20,92,139,162,180,239,249],"variant":[5],"of":[6,50,81,91,130,154,166,172,182,211,227,233,244],"k":[7,88,102],"nearest":[8,17,89,103],"neighbor":[9,18],"classification":[10,22,126],"on":[11,71,209],"large":[12],"many-class":[13],"high":[14],"dimensional":[15],"datasets.K":[16],"remains":[19],"popular":[21],"technique,":[23],"especially":[24],"in":[25,110,115,127,169],"areas":[26],"such":[27,41],"as":[28,42],"computer":[29],"vision,":[30],"drug":[31],"activity":[32],"prediction":[33,48],"and":[34,120,218,247],"astrophysics.":[35],"Furthermore,":[36],"many":[37,77],"more":[38],"modern":[39],"classifiers,":[40],"kernel-based":[43],"Bayes":[44],"classifiers":[45],"or":[46],"the":[47,72,79,83,87,128,145,152,167,170,173,190,204],"phase":[49],"SVMs,":[51],"require":[52,96],"computational":[53],"regimes":[54],"similar":[55],"to":[56,66,98,151,178,195,213],"k-NN.":[57],"We":[58],"believe":[59],"that":[60,74,200],"tractable":[61],"k-NN":[62,246,253],"algorithms":[63,117],"therefore":[64],"continue":[65],"be":[67,203],"important.This":[68],"relies":[70],"insight":[73,106],"even":[75],"with":[76,124,231],"classes,":[78],"task":[80],"finding":[82],"majority":[84],"class":[85],"among":[86],"neighbors":[90],"query":[93],"need":[94],"not":[95],"us":[97],"explicitly":[99],"find":[100],"those":[101],"neighbors.":[104],"was":[107],"previously":[108],"used":[109],"(Liu":[111],"et":[112],"al.,":[113],"2003)":[114],"two":[116,131],"called":[118],"KNS2":[119],"KNS3":[121],"which":[122],"dealt":[123],"fast":[125],"case":[129,153],"classes.":[132],"In":[133],"this":[134],"we":[136],"show":[137],"how":[138],"different":[140,164],"approach,":[141],"IOC":[142],"(standing":[143],"for":[144,186],"International":[146],"Olympic":[147],"Committee)":[148],"can":[149],"apply":[150],"n":[155,158],"classes":[156,199],"where":[157],">":[159],"2.IOC":[160],"assumes":[161],"slightly":[163],"processing":[165],"datapoints":[168],"neighborhood":[171],"query.":[174],"allows":[176],"it":[177,192],"search":[179],"set":[181],"metric":[183,254],"trees,":[184],"one":[185],"each":[187,232],"class.":[188],"During":[189],"searches":[191],"possible":[194],"quickly":[196],"prune":[197],"away":[198],"cannot":[201],"possibly":[202],"majority.We":[205],"give":[206],"experimental":[207],"results":[208],"datasets":[210],"up":[212],"5.8":[214],"x":[215,220],"105":[216],"records":[217],"1.5":[219],"103":[221],"attributes,":[222],"frequently":[223],"showing":[224],"an":[225],"order":[226],"magnitude":[228],"acceleration":[229],"compared":[230],"(i)":[234],"conventional":[235,245,252],"linear":[236],"scan,":[237],"(ii)":[238],"well-known":[240],"independent":[241],"SR-tree":[242],"implementation":[243],"(iii)":[248],"highly":[250],"optimized":[251],"tree":[255],"search.":[256]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2016-06-24T00:00:00"}