{"id":"https://openalex.org/W4414118233","doi":"https://doi.org/10.1109/fuzz62266.2025.11152120","title":"Calibrated Ellipsoidal Gaussian Clustering","display_name":"Calibrated Ellipsoidal Gaussian Clustering","publication_year":2025,"publication_date":"2025-07-06","ids":{"openalex":"https://openalex.org/W4414118233","doi":"https://doi.org/10.1109/fuzz62266.2025.11152120"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz62266.2025.11152120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11152120","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5002518694","display_name":"Thomas A. Runkler","orcid":"https://orcid.org/0000-0002-5465-198X"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas A. Runkler","raw_affiliation_strings":["Siemens AG,Munich,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens AG,Munich,Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100344706","display_name":"Wenlong Wu","orcid":"https://orcid.org/0000-0003-0467-2931"},"institutions":[{"id":"https://openalex.org/I862669128","display_name":"Xiaomi (China)","ror":"https://ror.org/029f7bn57","country_code":"CN","type":"company","lineage":["https://openalex.org/I862669128"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenlong Wu","raw_affiliation_strings":["Xiaomi Health Lab,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiaomi Health Lab,Beijing,China","institution_ids":["https://openalex.org/I862669128"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018303984","display_name":"James M. Keller","orcid":"https://orcid.org/0000-0002-0306-7142"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James M. Keller","raw_affiliation_strings":["University of Missouri–Columbia,Columbia,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri–Columbia,Columbia,USA","institution_ids":["https://openalex.org/I76835614"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9995999932289124,"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":0.9995999932289124,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9933000206947327,"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":0.9926000237464905,"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/ellipsoid","display_name":"Ellipsoid","score":0.777899980545044},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.703000009059906},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6152999997138977},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.599399983882904},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5016999840736389},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.47699999809265137},{"id":"https://openalex.org/keywords/determining-the-number-of-clusters-in-a-data-set","display_name":"Determining the number of clusters in a data set","score":0.4377000033855438},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.38679999113082886}],"concepts":[{"id":"https://openalex.org/C57489055","wikidata":"https://www.wikidata.org/wiki/Q190046","display_name":"Ellipsoid","level":2,"score":0.777899980545044},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.703000009059906},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6152999997138977},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.546999990940094},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5175999999046326},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5016999840736389},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C149872217","wikidata":"https://www.wikidata.org/wiki/Q5265701","display_name":"Determining the number of clusters in a data set","level":5,"score":0.4377000033855438},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C51267290","wikidata":"https://www.wikidata.org/wiki/Q5527848","display_name":"Gaussian random field","level":4,"score":0.3418999910354614},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3393000066280365},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C207968372","wikidata":"https://www.wikidata.org/wiki/Q310401","display_name":"k-means clustering","level":3,"score":0.2922999858856201},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27410000562667847},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz62266.2025.11152120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11152120","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2085992176","https://openalex.org/W2095973690","https://openalex.org/W2113076747","https://openalex.org/W2120688485","https://openalex.org/W2125836384","https://openalex.org/W2146795901","https://openalex.org/W2167428023","https://openalex.org/W2746095421","https://openalex.org/W2896971614"],"related_works":["https://openalex.org/W2005485125","https://openalex.org/W2390718407","https://openalex.org/W1986006778","https://openalex.org/W2034124193","https://openalex.org/W2379017606","https://openalex.org/W4211143546","https://openalex.org/W2171970890","https://openalex.org/W2356423988","https://openalex.org/W1964286703","https://openalex.org/W2169866437"],"abstract_inverted_index":{"This":[0,211],"paper":[1,56],"deals":[2],"with":[3,9,133,141],"the":[4,31,35,48,52,62,70,81,94,99,102,105,129,173,177,180,193,207,213],"problem":[5],"of":[6,61,80,98,108,112,138,146,179,188],"identifying":[7,221],"clusters":[8,100],"ellipsoidal":[10,27,88,95,109,197,222],"Gaussian":[11,72,89,96,110,198,223],"distributions,":[12,199],"which":[13,41,67,92],"occur":[14],"often":[15],"in":[16,101],"real":[17],"world":[18],"data.":[19,53,103],"Various":[20],"algorithms":[21,66],"have":[22],"been":[23],"proposed":[24,214],"to":[25,46,51,172,203],"find":[26],"clusters,":[28],"such":[29],"as":[30],"Gustafson\u2013Kessel":[32,36],"(GK)":[33],"and":[34,64,153,166,182],"Possibilistic":[37],"c\u2013Means":[38],"(GKPCM)":[39],"algorithms,":[40],"employ":[42],"an":[43,217],"iterative":[44],"mechanism":[45,125],"adapt":[47],"cluster":[49,131,162],"sizes":[50,132],"In":[54],"this":[55],"we":[57,68,115],"propose":[58],"a":[59,134],"modification":[60],"GK":[63,181],"GKPCM":[65,183],"call":[69],"calibrated":[71],"c\u2013":[73],"means":[74],"(CGCM)":[75],"algorithm.":[76],"A":[77],"specific":[78],"characteristic":[79],"CGCM":[82,118,160,200,215],"algorithm":[83,216],"is":[84,201],"that":[85,117,159,169],"it":[86],"yields":[87,128,161],"membership":[90],"values":[91],"match":[93,196],"distributions":[97],"For":[104,185],"general":[106],"case":[107],"data":[111,144,189,194],"arbitrary":[113],"dimensionality,":[114],"show":[116,158],"does":[119],"not":[120],"need":[121],"any":[122],"size":[123,148],"adaptation":[124],"but":[126],"automatically":[127],"correct":[130],"constant":[135],"scaling":[136],"factor":[137],"1/3.":[139],"Experiments":[140],"five":[142],"synthetic":[143],"sets":[145],"different":[147],"(100/1,000/10,000":[149],"points":[150],"per":[151],"cluster)":[152],"dimensionality":[154],"(2D,":[155],"3D,":[156],"4D)":[157],"estimates":[163],"(center":[164],"vectors":[165],"covariance":[167],"matrices)":[168],"are":[170],"closer":[171],"ground":[174,208],"truth":[175,209],"than":[176],"results":[178],"algorithms.":[184],"large":[186],"numbers":[187],"points,":[190],"i.e.":[191],"when":[192],"closely":[195],"able":[202],"very":[204],"accurately":[205],"identify":[206],"distributions.":[210],"makes":[212],"ideal":[218],"choice":[219],"for":[220],"clusters.":[224]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}