{"id":"https://openalex.org/W2979484321","doi":"https://doi.org/10.1109/access.2019.2946936","title":"Clustering of Remote Sensing Data Based on K-Nearest Neighbors Sampling With Non-Evenly Division","display_name":"Clustering of Remote Sensing Data Based on K-Nearest Neighbors Sampling With Non-Evenly Division","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2979484321","doi":"https://doi.org/10.1109/access.2019.2946936","mag":"2979484321"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2946936","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2946936","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08865019.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08865019.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100748129","display_name":"Lan Liu","orcid":"https://orcid.org/0000-0002-7525-5759"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lan Liu","raw_affiliation_strings":["School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-7525-5759","affiliations":[{"raw_affiliation_string":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035128762","display_name":"Chengfan Li","orcid":"https://orcid.org/0000-0001-9633-4475"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng-Fan Li","raw_affiliation_strings":["Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-9633-4475","affiliations":[{"raw_affiliation_string":"Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102015509","display_name":"Xiankun Sun","orcid":"https://orcid.org/0000-0002-4902-0067"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xian-Kun Sun","raw_affiliation_strings":["School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112121513","display_name":"Yongmei Lei","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong-Mei Lei","raw_affiliation_strings":["School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056934615","display_name":"Wen Si","orcid":"https://orcid.org/0000-0002-9569-6092"},"institutions":[{"id":"https://openalex.org/I153806059","display_name":"Shanghai Business School","ror":"https://ror.org/05kf5z787","country_code":"CN","type":"education","lineage":["https://openalex.org/I153806059"]},{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I4210159575","display_name":"Huashan Hospital","ror":"https://ror.org/05201qm87","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210159575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Si","raw_affiliation_strings":["Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai 200040, China","Shanghai Business School, College of Information and Computer Science, Shanghai 201400, China","Department of Rehabilitation, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai 200040, China","institution_ids":["https://openalex.org/I24943067","https://openalex.org/I4210159575"]},{"raw_affiliation_string":"Shanghai Business School, College of Information and Computer Science, Shanghai 201400, China","institution_ids":["https://openalex.org/I153806059"]},{"raw_affiliation_string":"Department of Rehabilitation, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003527229","display_name":"Ming-Shu Lai","orcid":null},"institutions":[{"id":"https://openalex.org/I6645349","display_name":"Southwest Minzu University","ror":"https://ror.org/04gaexw88","country_code":"CN","type":"education","lineage":["https://openalex.org/I6645349"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming-Shu Lai","raw_affiliation_strings":["School of Computer Science and Technology, Southwest Minzu University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest Minzu University, Chengdu, China","institution_ids":["https://openalex.org/I6645349"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16462709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"7","issue":null,"first_page":"147292","last_page":"147301"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.982699990272522,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7577121257781982},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6638200283050537},{"id":"https://openalex.org/keywords/division","display_name":"Division (mathematics)","score":0.6609654426574707},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6542050838470459},{"id":"https://openalex.org/keywords/distance-matrix","display_name":"Distance matrix","score":0.5819689035415649},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.581804633140564},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5662190318107605},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.545702338218689},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.53022700548172},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.5160557627677917},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5101450085639954},{"id":"https://openalex.org/keywords/sort","display_name":"sort","score":0.46270138025283813},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4602768123149872},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.44968101382255554},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.43285033106803894},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4232224225997925},{"id":"https://openalex.org/keywords/data-processing","display_name":"Data processing","score":0.4166325330734253},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33190470933914185},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3050922751426697},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29395562410354614},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15088635683059692},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.13807430863380432},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.09644284844398499},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08900529146194458}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577121257781982},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6638200283050537},{"id":"https://openalex.org/C60798267","wikidata":"https://www.wikidata.org/wiki/Q1226939","display_name":"Division (mathematics)","level":2,"score":0.6609654426574707},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6542050838470459},{"id":"https://openalex.org/C111208986","wikidata":"https://www.wikidata.org/wiki/Q901698","display_name":"Distance matrix","level":2,"score":0.5819689035415649},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.581804633140564},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5662190318107605},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.545702338218689},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.53022700548172},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.5160557627677917},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5101450085639954},{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.46270138025283813},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4602768123149872},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.44968101382255554},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.43285033106803894},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4232224225997925},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.4166325330734253},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33190470933914185},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3050922751426697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29395562410354614},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15088635683059692},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.13807430863380432},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09644284844398499},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08900529146194458},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","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},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2946936","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2946936","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08865019.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:dec3ddd8aa7e4b93b73181066b376259","is_oa":true,"landing_page_url":"https://doaj.org/article/dec3ddd8aa7e4b93b73181066b376259","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 147292-147301 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2946936","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2946936","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08865019.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5372072380","display_name":null,"funder_award_id":"U1811461","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8726124511","display_name":null,"funder_award_id":"41404024","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2979484321.pdf","grobid_xml":"https://content.openalex.org/works/W2979484321.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1969476713","https://openalex.org/W2016143382","https://openalex.org/W2062996541","https://openalex.org/W2078923569","https://openalex.org/W2082081125","https://openalex.org/W2100495367","https://openalex.org/W2117067575","https://openalex.org/W2143747537","https://openalex.org/W2163922914","https://openalex.org/W2164325628","https://openalex.org/W2248723555","https://openalex.org/W2253590344","https://openalex.org/W2308607294","https://openalex.org/W2389977017","https://openalex.org/W2420646957","https://openalex.org/W2438196771","https://openalex.org/W2551963638","https://openalex.org/W2558391528","https://openalex.org/W2564580785","https://openalex.org/W2592333355","https://openalex.org/W2715220489","https://openalex.org/W2722632382","https://openalex.org/W2737551799","https://openalex.org/W2763314014","https://openalex.org/W2789784903","https://openalex.org/W2791514669","https://openalex.org/W2792542990","https://openalex.org/W2793461576","https://openalex.org/W2794252992","https://openalex.org/W2801430703","https://openalex.org/W2804252870","https://openalex.org/W2811111681","https://openalex.org/W2890423234","https://openalex.org/W2891182845","https://openalex.org/W2894712623","https://openalex.org/W2898381489","https://openalex.org/W2899355278","https://openalex.org/W2900436152","https://openalex.org/W2900753462","https://openalex.org/W2903520265","https://openalex.org/W2908020915","https://openalex.org/W2913040965","https://openalex.org/W2917647353","https://openalex.org/W2918903593","https://openalex.org/W2919722487","https://openalex.org/W2921326779","https://openalex.org/W2921468651","https://openalex.org/W2928260953","https://openalex.org/W2932727603","https://openalex.org/W2937018631","https://openalex.org/W2938086739","https://openalex.org/W2940199079","https://openalex.org/W2944028616","https://openalex.org/W2944896556","https://openalex.org/W2962829974","https://openalex.org/W3103410140","https://openalex.org/W3216623900","https://openalex.org/W6760045681"],"related_works":["https://openalex.org/W1605858995","https://openalex.org/W2908582041","https://openalex.org/W4294328694","https://openalex.org/W2990663423","https://openalex.org/W2977833894","https://openalex.org/W3004382963","https://openalex.org/W3171568022","https://openalex.org/W2119897186","https://openalex.org/W4392016863","https://openalex.org/W2348550256"],"abstract_inverted_index":{"Data":[0],"computation":[1],"and":[2,11,26,36,68,80,99,113,121,128,143,152,155,177,183,206,216,230,254,262],"traffic":[3,185,229,257],"is":[4,106,175],"the":[5,22,31,38,63,70,82,89,92,111,115,122,132,149,157,162,168,172,180,193,201,210,218,227,233,240,247],"key":[6],"step":[7],"to":[8,135,167,246],"rapid":[9],"analysis":[10,32,249],"intelligent":[12],"transportation":[13],"application":[14],"based":[15,53],"on":[16,54],"remote":[17,34,50,251],"sensing":[18,35,51,252],"data.":[19],"To":[20],"tackle":[21],"low":[23],"computing":[24],"efficiency":[25],"high":[27,263],"storage":[28],"cost":[29],"in":[30,73,138],"of":[33,49,75,78,94,220,250,256],"improve":[37,232],"computational":[39],"performance":[40],"quickly,":[41],"this":[42],"paper":[43],"proposed":[44,194,241],"a":[45,100],"new":[46,101],"processing":[47,158],"method":[48,195,242],"data":[52,182,208,228,248,253],"k-nearest":[55],"neighbors":[56],"(KNN)":[57],"sampling":[58,150,212],"with":[59,91,125,140,213,259],"non-evenly":[60,86,214],"division.":[61,87],"In":[62],"method,":[64],"we":[65,109],"first":[66],"sort":[67],"preprocess":[69],"original":[71],"dataset":[72,84,151],"terms":[74],"any":[76],"size":[77],"one-dimension":[79],"segment":[81],"sample":[83,133,137,169],"by":[85,118,179,209],"Then":[88],"samples":[90,205],"range":[93],"boundary":[95,136],"width":[96],"are":[97],"reserved,":[98],"local":[102,123,144],"unsampled":[103,164,207],"mapping":[104,165],"table":[105,166],"reconstructed.":[107],"Next,":[108],"traverse":[110],"subset":[112],"compute":[114],"distance":[116,120,141],"matrix":[117,142],"Euclidean":[119],"density":[124],"descending":[126],"order,":[127],"further":[129],"determine":[130],"whether":[131],"belongs":[134],"accordance":[139],"density.":[145],"We":[146],"then":[147],"construct":[148],"combine":[153],"again":[154],"achieve":[156],"result":[159,237],"via":[160],"adding":[161],"entire":[163],"dataset.":[170],"Finally,":[171],"current":[173],"study":[174],"tested":[176],"verified":[178],"simulation":[181],"true":[184],"jam":[186,258],"prediction":[187,255],"case.":[188],"Our":[189],"experiments":[190],"present":[191],"that":[192,239],"not":[196],"only":[197],"can":[198,243],"record":[199],"precisely":[200],"correspondence":[202],"relations":[203],"between":[204],"KNN":[211],"division":[215],"ensure":[217],"accuracy":[219],"clustering":[221],"results,":[222],"but":[223],"also":[224],"significantly":[225],"reduce":[226],"effectively":[231],"memory":[234],"utilization.":[235],"The":[236],"reveals":[238],"potentially":[244],"contribute":[245],"large":[260],"scale":[261],"real-time":[264],"performance.":[265]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}