{"id":"https://openalex.org/W2940991801","doi":"https://doi.org/10.29007/trrg","title":"A New Method of Calculating Squared Euclidean Distance (SED) Using pTree Technology and Its Performance Analysis","display_name":"A New Method of Calculating Squared Euclidean Distance (SED) Using pTree Technology and Its Performance Analysis","publication_year":2019,"publication_date":"2019-03-13","ids":{"openalex":"https://openalex.org/W2940991801","doi":"https://doi.org/10.29007/trrg","mag":"2940991801"},"language":"en","primary_location":{"id":"doi:10.29007/trrg","is_oa":true,"landing_page_url":"https://doi.org/10.29007/trrg","pdf_url":"https://easychair.org/publications/open/qkPw","source":{"id":"https://openalex.org/S4220651395","display_name":"EPiC series in computing","issn_l":"2398-7340","issn":["2398-7340"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPiC Series in Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://easychair.org/publications/open/qkPw","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103842065","display_name":"Mohammad Kabir Hossain","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159608","display_name":"University of Minnesota Crookston","ror":"https://ror.org/04w6xt508","country_code":"US","type":"education","lineage":["https://openalex.org/I4210159608"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Hossain","raw_affiliation_strings":["Department of Math, Science and Technology University of Minnesota Crookston, Crookston, MN 56716, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Math, Science and Technology University of Minnesota Crookston, Crookston, MN 56716, USA","institution_ids":["https://openalex.org/I4210159608"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016067188","display_name":"Sameer Abufardeh","orcid":"https://orcid.org/0000-0002-9893-8923"},"institutions":[{"id":"https://openalex.org/I4210159608","display_name":"University of Minnesota Crookston","ror":"https://ror.org/04w6xt508","country_code":"US","type":"education","lineage":["https://openalex.org/I4210159608"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sameer Abufardeh","raw_affiliation_strings":["Department of Math, Science and Technology University of Minnesota Crookston, Crookston, MN 56716, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Math, Science and Technology University of Minnesota Crookston, Crookston, MN 56716, USA","institution_ids":["https://openalex.org/I4210159608"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5784,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.74968414,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"58","issue":null,"first_page":"45","last_page":"34"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","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"}},"topics":[{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","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"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9904999732971191,"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/T10057","display_name":"Face and Expression Recognition","score":0.9821000099182129,"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/euclidean-distance","display_name":"Euclidean distance","score":0.7881472110748291},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7020764350891113},{"id":"https://openalex.org/keywords/distance-measures","display_name":"Distance measures","score":0.6504533290863037},{"id":"https://openalex.org/keywords/closeness","display_name":"Closeness","score":0.6020826101303101},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5995513200759888},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.56130450963974},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.5578391551971436},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5203996896743774},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5085777044296265},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.4903557300567627},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47431087493896484},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45645827054977417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3734992742538452},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3281487822532654}],"concepts":[{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.7881472110748291},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7020764350891113},{"id":"https://openalex.org/C2639959","wikidata":"https://www.wikidata.org/wiki/Q1344778","display_name":"Distance measures","level":2,"score":0.6504533290863037},{"id":"https://openalex.org/C2779545769","wikidata":"https://www.wikidata.org/wiki/Q5135364","display_name":"Closeness","level":2,"score":0.6020826101303101},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5995513200759888},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.56130450963974},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.5578391551971436},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5203996896743774},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5085777044296265},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.4903557300567627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47431087493896484},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45645827054977417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3734992742538452},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3281487822532654},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.29007/trrg","is_oa":true,"landing_page_url":"https://doi.org/10.29007/trrg","pdf_url":"https://easychair.org/publications/open/qkPw","source":{"id":"https://openalex.org/S4220651395","display_name":"EPiC series in computing","issn_l":"2398-7340","issn":["2398-7340"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPiC Series in Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.29007/trrg","is_oa":true,"landing_page_url":"https://doi.org/10.29007/trrg","pdf_url":"https://easychair.org/publications/open/qkPw","source":{"id":"https://openalex.org/S4220651395","display_name":"EPiC series in computing","issn_l":"2398-7340","issn":["2398-7340"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPiC Series in Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2940991801.pdf","grobid_xml":"https://content.openalex.org/works/W2940991801.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W122120146","https://openalex.org/W348676241","https://openalex.org/W1536891989","https://openalex.org/W1986198395","https://openalex.org/W2061681234","https://openalex.org/W2764576520","https://openalex.org/W2979792551","https://openalex.org/W2980887507","https://openalex.org/W4246603859","https://openalex.org/W6600182862"],"related_works":["https://openalex.org/W3038204311","https://openalex.org/W2186101196","https://openalex.org/W2129888254","https://openalex.org/W3039354370","https://openalex.org/W2115336194","https://openalex.org/W2387908128","https://openalex.org/W2133920285","https://openalex.org/W4294624291","https://openalex.org/W1563946824","https://openalex.org/W2940991801"],"abstract_inverted_index":{"One":[0,57],"of":[1,4,99,143],"the":[2,11,27,30,76,82,97,141,148,152,169,183],"advantages":[3],"Euclidean":[5,133],"distance":[6,13,31,68,150],"is":[7,23,46,60],"that":[8,61],"it":[9,22,196],"measures":[10,69],"regular":[12],"between":[14,32,84,151],"two":[15],"points":[16,34,102,116],"in":[17,26,62,90,117,121,159,187],"space.":[18],"For":[19,88],"this":[20,44,125,173],"reason,":[21],"widely":[24],"used":[25,79,138,158],"applications":[28,66],"where":[29],"data":[33,64,86,101,153,185,200],"are":[35,70,78],"needed":[36],"to":[37,40,80,103,113,139,146,167],"be":[38,137,180],"calculated":[39,181],"measure":[41,96],"similarities.":[42],"However,":[43],"method":[45],"costly":[47],"as":[48,73,75,131],"there":[49],"involve":[50],"expensive":[51],"square":[52,54,144],"and":[53,92,164,194],"root":[55,145],"operations.":[56],"useful":[58],"observation":[59],"many":[63],"mining":[65],"absolute":[67],"not":[71],"necessary":[72],"long":[74],"distances":[77,98,106],"compare":[81,104],"closeness":[83],"various":[85],"points.":[87,154],"example,":[89],"classification":[91],"clustering,":[93,161],"we":[94,175],"often":[95],"multiple":[100],"their":[105],"from":[107,111],"known":[108,130],"classes":[109],"or":[110,120],"centroids":[112],"assign":[114],"those":[115],"a":[118,122],"class":[119],"cluster.":[123],"In":[124,172],"regards,":[126],"an":[127],"alternative":[128],"approach":[129],"Squared":[132],"Distance":[134],"(SED)":[135],"can":[136,179],"avoid":[140],"computation":[142],"get":[147],"squared":[149],"SED":[155,178],"has":[156],"been":[157],"classification,":[160],"image":[162],"processing,":[163],"other":[165],"areas":[166],"save":[168],"computational":[170],"expenses.":[171],"paper,":[174],"show":[176],"how":[177],"for":[182],"vertical":[184],"represented":[186],"pTrees.":[188],"We":[189],"also":[190],"analyze":[191],"its":[192],"performance":[193],"compared":[195],"with":[197],"traditional":[198],"horizontal":[199],"representation.":[201]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}