{"id":"https://openalex.org/W2071927118","doi":"https://doi.org/10.1109/sisy.2014.6923614","title":"Using total correlation to discover related clusters of clinical chemistry parameters","display_name":"Using total correlation to discover related clusters of clinical chemistry parameters","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W2071927118","doi":"https://doi.org/10.1109/sisy.2014.6923614","mag":"2071927118"},"language":"en","primary_location":{"id":"doi:10.1109/sisy.2014.6923614","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sisy.2014.6923614","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY)","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/A5019524154","display_name":"Tam\u00e1s Ferenci","orcid":"https://orcid.org/0000-0001-6791-3080"},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Tamas Ferenci","raw_affiliation_strings":["Physiological Controls Group Obuda University Budapest, Hungary","Physiological Controls Group, \u00d3buda University Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Physiological Controls Group Obuda University Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]},{"raw_affiliation_string":"Physiological Controls Group, \u00d3buda University Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021051122","display_name":"Levente Kov\u00e1cs","orcid":"https://orcid.org/0000-0002-3188-0800"},"institutions":[{"id":"https://openalex.org/I103356709","display_name":"Obuda University","ror":"https://ror.org/00ax71d21","country_code":"HU","type":"education","lineage":["https://openalex.org/I103356709"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Levente Kovacs","raw_affiliation_strings":["Physiological Controls Group Obuda University, Budapest, Hungary","Physiological Controls Group, \u00d3buda University Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Physiological Controls Group Obuda University, Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]},{"raw_affiliation_string":"Physiological Controls Group, \u00d3buda University Budapest, Hungary","institution_ids":["https://openalex.org/I103356709"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5019524154"],"corresponding_institution_ids":["https://openalex.org/I103356709"],"apc_list":null,"apc_paid":null,"fwci":0.819,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.82866401,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"6","issue":null,"first_page":"49","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9973000288009644,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9973000288009644,"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/T12114","display_name":"Sensory Analysis and Statistical Methods","score":0.9764000177383423,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9721999764442444,"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/univariate","display_name":"Univariate","score":0.7214688658714294},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.6786805987358093},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5938328504562378},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5054118633270264},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39723512530326843},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34901732206344604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3336639404296875},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32173675298690796},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30674076080322266},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.24240681529045105}],"concepts":[{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.7214688658714294},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6786805987358093},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5938328504562378},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5054118633270264},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39723512530326843},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34901732206344604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3336639404296875},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32173675298690796},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30674076080322266},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.24240681529045105},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/sisy.2014.6923614","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sisy.2014.6923614","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY)","raw_type":"proceedings-article"},{"id":"pmh:oai:real.mtak.hu:19654","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400081","display_name":"Repository of the Academy's Library (Library of the Hungarian Academy of Sciences)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210140733","host_organization_name":"Library and Information Centre of the Hungarian Academy of Sciences","host_organization_lineage":["https://openalex.org/I4210140733"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/bookPart"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322192","display_name":"Magyar Tudom\u00e1nyos Akad\u00e9mia","ror":"https://ror.org/02ks8qq67"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W635307206","https://openalex.org/W1480376833","https://openalex.org/W1484413656","https://openalex.org/W1507178887","https://openalex.org/W1655058335","https://openalex.org/W1965555277","https://openalex.org/W1969116803","https://openalex.org/W1985514943","https://openalex.org/W1994063459","https://openalex.org/W2006533296","https://openalex.org/W2007995029","https://openalex.org/W2016381774","https://openalex.org/W2023205960","https://openalex.org/W2042035594","https://openalex.org/W2049017883","https://openalex.org/W2056795330","https://openalex.org/W2075843680","https://openalex.org/W2078483536","https://openalex.org/W2095439994","https://openalex.org/W2099111195","https://openalex.org/W2113952909","https://openalex.org/W2124379907","https://openalex.org/W2135191607","https://openalex.org/W2145979309","https://openalex.org/W2151967815","https://openalex.org/W2159401492","https://openalex.org/W2210387432","https://openalex.org/W2336509789","https://openalex.org/W2337948133","https://openalex.org/W2343887538","https://openalex.org/W2479531384","https://openalex.org/W2502366096","https://openalex.org/W2612166593","https://openalex.org/W2787894218","https://openalex.org/W2887002214","https://openalex.org/W3015962962","https://openalex.org/W3106063097","https://openalex.org/W3123920865","https://openalex.org/W3145128584","https://openalex.org/W4236706032","https://openalex.org/W4239181501","https://openalex.org/W4246219036","https://openalex.org/W4248751457","https://openalex.org/W6628750762","https://openalex.org/W6630293821","https://openalex.org/W6679705636","https://openalex.org/W6703140735"],"related_works":["https://openalex.org/W1828158523","https://openalex.org/W2047547195","https://openalex.org/W204175656","https://openalex.org/W2911841387","https://openalex.org/W2803255289","https://openalex.org/W1512294453","https://openalex.org/W1993992974","https://openalex.org/W2550734047","https://openalex.org/W2176526134","https://openalex.org/W2341571017"],"abstract_inverted_index":{"Clinical":[0],"chemistry":[1,108,180],"tests":[2],"are":[3,60],"widely":[4],"used":[5],"in":[6,13],"medical":[7],"diagnosis.":[8],"Physicians":[9],"typically":[10],"interpret":[11],"them":[12],"a":[14,22,86,167,189],"univariate":[15],"sense,":[16],"by":[17,52],"comparing":[18],"each":[19],"parameter":[20],"to":[21,89,102,132],"reference":[23],"interval,":[24],"however,":[25],"their":[26],"correlation":[27,45,101,112],"structure":[28],"may":[29],"also":[30],"be":[31],"interesting,":[32],"as":[33,110,125,159],"it":[34,128],"can":[35,113,144,173],"shed":[36],"light":[37],"on":[38,188],"common":[39],"physiologic":[40],"or":[41,121],"pathological":[42],"mechanisms.":[43],"The":[44,182],"analysis":[46],"of":[47,64,71,79,136,151,157,164,178],"such":[48,72],"parameters":[49],"is":[50,74,129,141,143,170,184],"hindered":[51],"two":[53],"problems:":[54],"the":[55,58,69,77,104,118,133,137,162,175,193],"relationships":[56],"between":[57,106,117,149,154],"variables":[59,73,158],"sometimes":[61],"non-linear":[62,120],"and":[63,68,186],"unknown":[65],"functional":[66],"form,":[67],"number":[70],"high,":[75],"making":[76],"use":[78],"classical":[80],"tools":[81],"infeasible.":[82],"This":[83],"paper":[84],"presents":[85],"novel":[87,168],"approach":[88,169,183],"address":[90],"both":[91],"problems.":[92],"It":[93],"uses":[94],"an":[95],"information":[96],"theory-based":[97],"measure":[98],"called":[99],"total":[100,111],"quantify":[103,145],"dependence":[105,116,146],"clinical":[107,179],"variables,":[109,119,152],"detect":[114],"any":[115],"even":[122],"non-monotone":[123],"ones":[124],"well,":[126],"hence":[127],"completely":[130],"insensitive":[131],"actual":[134],"nature":[135],"relationship.":[138],"Another":[139],"advantage":[140],"that":[142,172],"not":[147],"only":[148],"pairs":[150],"but":[153],"larger":[155],"groups":[156],"well.":[160],"By":[161],"virtue":[163],"this":[165],"fact,":[166],"presented":[171],"handle":[174],"high":[176],"dimensionality":[177],"parameters.":[181],"implemented":[185],"illustrated":[187],"real-life":[190],"database":[191],"from":[192],"representative":[194],"US":[195],"public":[196],"health":[197],"survey":[198],"NHANES.":[199]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}