{"id":"https://openalex.org/W2106767554","doi":"https://doi.org/10.1109/tpami.1986.4767859","title":"A Fast k Nearest Neighbor Finding Algorithm Based on the Ordered Partition","display_name":"A Fast k Nearest Neighbor Finding Algorithm Based on the Ordered Partition","publication_year":1986,"publication_date":"1986-11-01","ids":{"openalex":"https://openalex.org/W2106767554","doi":"https://doi.org/10.1109/tpami.1986.4767859","mag":"2106767554","pmid":"https://pubmed.ncbi.nlm.nih.gov/21869372"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.1986.4767859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.1986.4767859","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5035459193","display_name":"Baek S. Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210152466","display_name":"Hanyang University Medical Center","ror":"https://ror.org/05tn05n57","country_code":"KR","type":"healthcare","lineage":["https://openalex.org/I4210152466","https://openalex.org/I4575257"]},{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Baek S. Kim","raw_affiliation_strings":["Department of Medical Information Management, College of Medicine, Hanyang University, 17 Haengdangdong, Seong-dong-ku, Seoul 133, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medical Information Management, College of Medicine, Hanyang University, 17 Haengdangdong, Seong-dong-ku, Seoul 133, Korea","institution_ids":["https://openalex.org/I4575257","https://openalex.org/I4210152466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081657525","display_name":"Song B. Park","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Song B. Park","raw_affiliation_strings":["Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Seoul, South Korea","Department of Electrical Engineering , Korea Advanced Institute of Science and Technology , P.O. Box 150, Chongyangni, Seoul, 131, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Seoul, South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"Department of Electrical Engineering , Korea Advanced Institute of Science and Technology , P.O. Box 150, Chongyangni, Seoul, 131, Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5035459193"],"corresponding_institution_ids":["https://openalex.org/I4210152466","https://openalex.org/I4575257"],"apc_list":null,"apc_paid":null,"fwci":2.9526,"has_fulltext":false,"cited_by_count":109,"citation_normalized_percentile":{"value":0.92442102,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"PAMI-8","issue":"6","first_page":"761","last_page":"766"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9980999827384949,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9980999827384949,"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/T10057","display_name":"Face and Expression Recognition","score":0.9950000047683716,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.7718920111656189},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.719559907913208},{"id":"https://openalex.org/keywords/nearest-neighbor-chain-algorithm","display_name":"Nearest-neighbor chain algorithm","score":0.7172268033027649},{"id":"https://openalex.org/keywords/best-bin-first","display_name":"Best bin first","score":0.682335615158081},{"id":"https://openalex.org/keywords/nearest-neighbor-graph","display_name":"Nearest neighbor graph","score":0.6811114549636841},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.636884331703186},{"id":"https://openalex.org/keywords/large-margin-nearest-neighbor","display_name":"Large margin nearest neighbor","score":0.5594801902770996},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5535510182380676},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5217403173446655},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.47570639848709106},{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.43417826294898987},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.42739859223365784},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3378516137599945},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.331917405128479},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23855867981910706},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.10416042804718018},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09834328293800354}],"concepts":[{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.7718920111656189},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.719559907913208},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.7172268033027649},{"id":"https://openalex.org/C161986146","wikidata":"https://www.wikidata.org/wiki/Q4896845","display_name":"Best bin first","level":3,"score":0.682335615158081},{"id":"https://openalex.org/C90988772","wikidata":"https://www.wikidata.org/wiki/Q2855103","display_name":"Nearest neighbor graph","level":3,"score":0.6811114549636841},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.636884331703186},{"id":"https://openalex.org/C94475309","wikidata":"https://www.wikidata.org/wiki/Q6489154","display_name":"Large margin nearest neighbor","level":3,"score":0.5594801902770996},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5535510182380676},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5217403173446655},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47570639848709106},{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.43417826294898987},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.42739859223365784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3378516137599945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.331917405128479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23855867981910706},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.10416042804718018},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09834328293800354},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.0},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.1986.4767859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.1986.4767859","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:21869372","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/21869372","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321142","display_name":"Hanyang University","ror":"https://ror.org/046865y68"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1983645263","https://openalex.org/W2024668293","https://openalex.org/W2050913223","https://openalex.org/W2084354115","https://openalex.org/W2088171019","https://openalex.org/W2122111042","https://openalex.org/W2149894778","https://openalex.org/W2799119698"],"related_works":["https://openalex.org/W2182477562","https://openalex.org/W2795392346","https://openalex.org/W2351157934","https://openalex.org/W4246757943","https://openalex.org/W2375128115","https://openalex.org/W1595303882","https://openalex.org/W1558159560","https://openalex.org/W2969538544","https://openalex.org/W2245581955","https://openalex.org/W17346433"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,65,90,99],"fast":[3],"nearest":[4,62,91],"neighbor":[5,92],"finding":[6],"algorithm,":[7],"named":[8],"tentatively":[9],"an":[10],"ordered":[11,16,27],"partition,":[12],"based":[13],"on":[14,83],"the":[15,19,36,40,45,56,72,84],"lists":[17],"of":[18,22],"training":[20],"samples":[21,47,96],"each":[23],"projection":[24],"axis.":[25],"The":[26],"partition":[28],"contains":[29],"two":[30],"properties,":[31],"one":[32],"is":[33,42,53,74],"ordering\u00bfto":[34],"bound":[35],"search":[37],"region,":[38],"and":[39,78],"other":[41],"partitioning\u00bfto":[43],"reject":[44],"unwanted":[46],"without":[48],"actual":[49],"distance":[50,81],"computations.":[51],"It":[52],"proved":[54],"that":[55,71],"proposed":[57],"algorithm":[58,73],"can":[59],"find":[60,89],"k":[61],"neighbors":[63],"in":[64],"constant":[66],"expected":[67],"time.":[68],"Simulations":[69],"show":[70],"rather":[75],"distribution":[76],"free,":[77],"only":[79],"4.6":[80],"calculations,":[82],"average,":[85],"were":[86],"required":[87],"to":[88],"among":[93],"10":[94],"000":[95],"drawn":[97],"from":[98],"bivariate":[100],"normal":[101],"distribution.":[102]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
