{"id":"https://openalex.org/W2564280976","doi":null,"title":"Robust Asymmetric Clustering","display_name":"Robust Asymmetric Clustering","publication_year":2014,"publication_date":"2014-02-26","ids":{"openalex":"https://openalex.org/W2564280976","mag":"2564280976"},"language":"en","primary_location":{"id":"mag:2564280976","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1402.6744.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1402.6744.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033231291","display_name":"Katherine Morris","orcid":"https://orcid.org/0000-0003-3981-1567","observed_orcids":["https://orcid.org/0000-0003-3981-1567"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Katherine Morris","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3981-1567","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069386158","display_name":"Paul D. McNicholas","orcid":"https://orcid.org/0000-0002-2482-523X","observed_orcids":["https://orcid.org/0000-0002-2482-523X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paul D. McNicholas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009939605","display_name":"Antonio Punzo","orcid":"https://orcid.org/0000-0001-7742-1821","observed_orcids":["https://orcid.org/0000-0001-7742-1821"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antonio Punzo","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-7742-1821","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007643370","display_name":"Ryan P. Browne","orcid":"https://orcid.org/0000-0003-4543-0218","observed_orcids":["https://orcid.org/0000-0003-4543-0218"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ryan P. Browne","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4543-0218","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9994999766349792,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9994999766349792,"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":0.00039999998989515007,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/outliers","display_name":"outliers","score":0.45399999618530273},{"id":"https://openalex.org/keywords/noise","display_name":"noise","score":0.6620000004768372},{"id":"https://openalex.org/keywords/skew-normal-distribution","display_name":"skew-normal distribution","score":0.8149999976158142},{"id":"https://openalex.org/keywords/model-based-clustering","display_name":"model-based clustering","score":0.734000027179718},{"id":"https://openalex.org/keywords/expectation-conditional-maximization","display_name":"expectation conditional maximization","score":0.8619999885559082},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.8700000047683716},{"id":"https://openalex.org/keywords/number-of-components-selection","display_name":"number of components selection","score":0.5799999833106995}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8122348785400391},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.7626054286956787},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7430989742279053},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6551929712295532},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.6018984317779541},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5014238357543945},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.4952634871006012},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.488471657037735},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4628853499889374},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4476378560066223},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4457840025424957},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41487592458724976},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3915804326534271},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35129696130752563},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3225347399711609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2896965742111206},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23494428396224976},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.17716538906097412},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"mag:2564280976","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1402.6744.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.763.3609","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.763.3609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1402.6744.pdf","raw_type":"text"}],"best_oa_location":{"id":"mag:2564280976","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1402.6744.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W159361115","https://openalex.org/W1583397890","https://openalex.org/W1755019093","https://openalex.org/W1976969815","https://openalex.org/W1996094544","https://openalex.org/W2033403400","https://openalex.org/W2035886438","https://openalex.org/W2039615557","https://openalex.org/W2049633694","https://openalex.org/W2073984943","https://openalex.org/W2085808540","https://openalex.org/W2104433707","https://openalex.org/W2105787212","https://openalex.org/W2123781015","https://openalex.org/W2128540811","https://openalex.org/W2146855196","https://openalex.org/W2152904625","https://openalex.org/W2157324002","https://openalex.org/W2474170597","https://openalex.org/W2797555166","https://openalex.org/W2936317893","https://openalex.org/W3103302203"],"related_works":["https://openalex.org/W2798753876","https://openalex.org/W4384161795","https://openalex.org/W1520024071","https://openalex.org/W2936317893","https://openalex.org/W3216684315","https://openalex.org/W2403172445","https://openalex.org/W2262842515","https://openalex.org/W4390639551","https://openalex.org/W2607018838","https://openalex.org/W2423680768"],"abstract_inverted_index":{"Contaminated":[0],"mixture":[1,26,36,44],"models":[2],"are":[3,90],"developed":[4],"for":[5,92],"model-based":[6],"clustering":[7],"of":[8,27,37,52,66,72,98,109],"data":[9],"with":[10],"asymmetric":[11,30],"clusters":[12],"as":[13,15],"well":[14],"spurious":[16,56],"points,":[17,57],"outliers,":[18,58],"and/or":[19,59],"noise.":[20],"Specifically,":[21],"we":[22],"introduce":[23],"a":[24,34,47,85],"contaminated":[25,28,35,38],"shifted":[29],"Laplace":[31],"dis-tributions":[32],"and":[33,61,95,116],"skew-normal":[39],"distributions.":[40],"In":[41],"each":[42],"case,":[43],"components":[45,99],"have":[46,81],"parameter":[48,93],"controlling":[49],"the":[50,64,96,103],"proportion":[51],"bad":[53],"points":[54],"(i.e.,":[55],"noise)":[60],"one":[62],"specifying":[63],"degree":[65],"contamination.":[67],"A":[68],"very":[69],"important":[70],"feature":[71],"our":[73,110],"approaches":[74,111],"is":[75,100,112],"that":[76],"these":[77],"parameters":[78],"do":[79],"not":[80],"to":[82],"be":[83],"specified":[84],"priori.":[86],"Expectation-conditional":[87],"maximization":[88],"algorithms":[89],"outlined":[91],"estimation":[94],"number":[97],"selected":[101],"using":[102],"Bayesian":[104],"information":[105],"criterion.":[106],"The":[107],"performance":[108],"illustrated":[113],"on":[114],"artificial":[115],"real":[117],"data.":[118]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2017-01-06T00:00:00"}