{"id":"https://openalex.org/W2307159622","doi":"https://doi.org/10.1109/smc.2016.7844219","title":"Empirical data analysis: A new tool for data analytics","display_name":"Empirical data analysis: A new tool for data analytics","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2307159622","doi":"https://doi.org/10.1109/smc.2016.7844219","mag":"2307159622"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2016.7844219","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2016.7844219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.lancs.ac.uk/id/eprint/80044/1/1008.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039480864","display_name":"Plamen Angelov","orcid":"https://orcid.org/0000-0002-5770-934X"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Plamen Angelov","raw_affiliation_strings":["Data Science Group, Lancaster University, Lancaster, UK"],"affiliations":[{"raw_affiliation_string":"Data Science Group, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100623335","display_name":"Xiaowei Gu","orcid":"https://orcid.org/0000-0001-9116-4761"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiaowei Gu","raw_affiliation_strings":["Data Science Group, Lancaster University, Lancaster, UK"],"affiliations":[{"raw_affiliation_string":"Data Science Group, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079755748","display_name":"Dmitry Kangin","orcid":"https://orcid.org/0000-0001-9769-7585"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dmitry Kangin","raw_affiliation_strings":["Data Science Group, Lancaster University, Lancaster, UK"],"affiliations":[{"raw_affiliation_string":"Data Science Group, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019504861","display_name":"Jos\u00e9 C. Pr\u0131\u0301ncipe","orcid":"https://orcid.org/0000-0002-3449-3531"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jose Principe","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Florida, Grainsville, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Florida, Grainsville, FL, USA","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5039480864"],"corresponding_institution_ids":["https://openalex.org/I67415387"],"apc_list":null,"apc_paid":null,"fwci":5.8552,"has_fulltext":true,"cited_by_count":49,"citation_normalized_percentile":{"value":0.96729081,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9965000152587891,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9965000152587891,"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/T10320","display_name":"Neural Networks and Applications","score":0.9886999726295471,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9882000088691711,"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":0.6916612982749939},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.6213356256484985},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5668033957481384},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.5091456770896912},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.4832340180873871},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.4668724238872528},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4570056200027466},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4453844428062439},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3973134458065033},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3606458306312561},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2476930320262909},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2354333996772766},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.136527419090271}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6916612982749939},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.6213356256484985},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5668033957481384},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.5091456770896912},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4832340180873871},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.4668724238872528},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4570056200027466},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4453844428062439},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3973134458065033},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3606458306312561},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2476930320262909},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2354333996772766},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.136527419090271},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/smc.2016.7844219","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2016.7844219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.lancs.ac.uk:80044","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.lancs.ac.uk/id/eprint/80044/1/1008.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"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":"PeerReviewed"},{"id":"pmh:oai:kar.kent.ac.uk:90177","is_oa":false,"landing_page_url":"https://kar.kent.ac.uk/90177/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196264","display_name":"Kent Academic Repository (University of Kent)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20581793","host_organization_name":"University of Kent","host_organization_lineage":["https://openalex.org/I20581793"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference or workshop item"}],"best_oa_location":{"id":"pmh:oai:eprints.lancs.ac.uk:80044","is_oa":true,"landing_page_url":null,"pdf_url":"https://eprints.lancs.ac.uk/id/eprint/80044/1/1008.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"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":"PeerReviewed"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2307159622.pdf","grobid_xml":"https://content.openalex.org/works/W2307159622.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W648613739","https://openalex.org/W1563088657","https://openalex.org/W1821080089","https://openalex.org/W1983989471","https://openalex.org/W2018189414","https://openalex.org/W2171033594","https://openalex.org/W2488504005","https://openalex.org/W2540438180","https://openalex.org/W3083113686","https://openalex.org/W4211007335","https://openalex.org/W4230367971","https://openalex.org/W4230900352","https://openalex.org/W4250859275"],"related_works":["https://openalex.org/W2347703430","https://openalex.org/W3148227991","https://openalex.org/W3001521712","https://openalex.org/W4210727352","https://openalex.org/W2380470746","https://openalex.org/W1486593826","https://openalex.org/W2134683619","https://openalex.org/W2967381224","https://openalex.org/W2123203558","https://openalex.org/W4238028212"],"abstract_inverted_index":{"In":[0,87],"this":[1,88,216],"paper,":[2],"a":[3,92,203,220,349],"novel":[4],"empirical":[5,232],"data":[6,110,114,127,134,139,157,167,177,201,306,316,324],"analysis":[7],"approach":[8,101],"(abbreviated":[9],"as":[10,185,252,254,289,291,344,346],"EDA)":[11],"is":[12,15,32,40,103,142,213,238,262],"introduced":[13],"which":[14,46],"entirely":[16,229],"data-driven":[17],"and":[18,23,29,50,98,129,153,194,308,341,358],"free":[19],"from":[20,230,243,246,255],"restricting":[21],"assumptions":[22,45],"pre-defined":[24],"problem-":[25,75],"or":[26,76,79,144],"user-specific":[27,77],"parameters":[28,78],"thresholds.":[30],"It":[31,218],"well":[33,253,290,345],"known":[34],"that":[35,102,169,224,318],"the":[36,61,67,106,109,113,118,126,133,147,172,176,188,191,195,231,244,247,273,277,281,313,323,336],"traditional":[37],"probability":[38,248],"theory":[39],"restricted":[41],"by":[42],"strong":[43],"prior":[44],"are":[47,64],"often":[48],"impractical":[49],"do":[51],"not":[52,320],"hold":[53],"in":[54,108,132,175,215,322],"real":[55,68],"problems.":[56],"Machine":[57],"learning":[58,343],"methods,":[59],"on":[60,74,105],"other":[62,293,359],"hand,":[63],"closer":[65],"to":[66,146,265,271,305,334,347],"problems":[69],"but":[70],"they":[71],"usually":[72],"rely":[73],"thresholds":[80],"making":[81,302],"it":[82,237,303],"rather":[83],"art":[84],"than":[85],"science.":[86],"paper":[89],"we":[90,123],"introduce":[91],"theoretically":[93],"sound":[94],"yet":[95],"practically":[96],"unrestricted":[97],"widely":[99],"applicable":[100,304],"based":[104],"density":[107,193,249],"space.":[111,135],"Since":[112],"may":[115],"have":[116,170],"exactly":[117,171],"same":[119,173],"value":[120],"multiple":[121],"times":[122],"distinguish":[124],"between":[125],"points":[128,168,317],"unique":[130,150,161],"locations":[131],"The":[136,163],"number":[137,148,164],"of":[138,149,165,190,197,199,234,283,287,312,315,339,351],"points,":[140,202],"k":[141],"larger":[143],"equal":[145],"locations,":[151],"l":[152],"at":[154],"least":[155],"one":[156],"point":[158],"occupies":[159],"each":[160],"location.":[162],"different":[166],"location":[174],"space":[178],"(equal":[179],"value),":[180],"f":[181],"can":[182,298,327],"be":[183,299,328],"seen":[184],"frequency.":[186],"Through":[187],"combination":[189],"spatial":[192],"frequency":[196],"occurrence":[198],"discrete":[200],"new":[204,331],"concept":[205,332],"called":[206],"multimodal":[207,274],"typicality,":[208],"\u03c4":[209],"<sup":[210],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[211],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">MM</sup>":[212],"proposed":[214],"paper.":[217],"offers":[219],"closed":[221,278],"analytical":[222],"form":[223,279],"represents":[225],"ensemble":[226],"properties":[227],"derived":[228],"observations":[233],"data.":[235],"Moreover,":[236,276],"very":[239,337],"close":[240],"(yet":[241],"different)":[242],"histograms,":[245],"function":[250],"(pdf)":[251],"fuzzy":[256],"set":[257],"membership":[258],"functions.":[259],"Remarkably,":[260],"there":[261],"no":[263],"need":[264],"perform":[266],"complicated":[267],"pre-processing":[268],"like":[269],"clustering":[270],"get":[272],"representation.":[275],"for":[280],"case":[282],"Euclidean,":[284],"Mahalanobis":[285],"type":[286],"distance":[288],"some":[292],"forms":[294],"(e.g.":[295],"cosine-based":[296],"dissimilarity)":[297],"expressed":[300],"recursively":[301],"streams":[307],"online":[309],"algorithms.":[310,360],"Inference/estimation":[311],"typicality":[314],"were":[319],"present":[321],"so":[325],"far":[326],"made.":[329],"This":[330],"allows":[333],"rethink":[335],"foundations":[338],"statistical":[340],"machine":[342],"develop":[348],"series":[350],"anomaly":[352],"detection,":[353],"clustering,":[354],"classification,":[355],"prediction,":[356],"control":[357]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":5}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
