{"id":"https://openalex.org/W2996085951","doi":"https://doi.org/10.1080/03610918.2019.1697449","title":"SingleCross-clustering: an algorithm for finding elongated clusters with automatic estimation of outliers and number of clusters","display_name":"SingleCross-clustering: an algorithm for finding elongated clusters with automatic estimation of outliers and number of clusters","publication_year":2019,"publication_date":"2019-12-12","ids":{"openalex":"https://openalex.org/W2996085951","doi":"https://doi.org/10.1080/03610918.2019.1697449","mag":"2996085951"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2019.1697449","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1697449","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-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/A5069020262","display_name":"Paola Tellaroli","orcid":"https://orcid.org/0000-0002-5112-9256"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"education","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"P. Tellaroli","raw_affiliation_strings":["Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Padova, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Padova, Italy","institution_ids":["https://openalex.org/I138689650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5069020262"],"corresponding_institution_ids":["https://openalex.org/I138689650"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.16622915,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"51","issue":"5","first_page":"2412","last_page":"2428"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9987000226974487,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8848979473114014},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.781813383102417},{"id":"https://openalex.org/keywords/single-linkage-clustering","display_name":"Single-linkage clustering","score":0.6647675037384033},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.6453936100006104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6022283434867859},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5897239446640015},{"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":0.5780032277107239},{"id":"https://openalex.org/keywords/complete-linkage-clustering","display_name":"Complete-linkage clustering","score":0.5711442232131958},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5446377396583557},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4969189465045929},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42644333839416504},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4164424538612366},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.41404736042022705},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2967867851257324}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8848979473114014},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.781813383102417},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.6647675037384033},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.6453936100006104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6022283434867859},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5897239446640015},{"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":0.5780032277107239},{"id":"https://openalex.org/C23822008","wikidata":"https://www.wikidata.org/wiki/Q5156437","display_name":"Complete-linkage clustering","level":5,"score":0.5711442232131958},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5446377396583557},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4969189465045929},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42644333839416504},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4164424538612366},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.41404736042022705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2967867851257324},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2019.1697449","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2019.1697449","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W45832856","https://openalex.org/W174354292","https://openalex.org/W194523672","https://openalex.org/W436398176","https://openalex.org/W1270205671","https://openalex.org/W1493454437","https://openalex.org/W1510444525","https://openalex.org/W1604645467","https://openalex.org/W1829970718","https://openalex.org/W1967641500","https://openalex.org/W1972498398","https://openalex.org/W1975152892","https://openalex.org/W1977556410","https://openalex.org/W2000735875","https://openalex.org/W2001850277","https://openalex.org/W2006255103","https://openalex.org/W2011420344","https://openalex.org/W2011832962","https://openalex.org/W2016381774","https://openalex.org/W2022237013","https://openalex.org/W2026513874","https://openalex.org/W2033403400","https://openalex.org/W2039912481","https://openalex.org/W2048675583","https://openalex.org/W2068535610","https://openalex.org/W2070409741","https://openalex.org/W2072533619","https://openalex.org/W2078358781","https://openalex.org/W2082503527","https://openalex.org/W2082823924","https://openalex.org/W2085423423","https://openalex.org/W2086338123","https://openalex.org/W2090634555","https://openalex.org/W2091858563","https://openalex.org/W2100958137","https://openalex.org/W2113249190","https://openalex.org/W2127218421","https://openalex.org/W2134616382","https://openalex.org/W2153233077","https://openalex.org/W2154104079","https://openalex.org/W2158742424","https://openalex.org/W2291624323","https://openalex.org/W2310792464","https://openalex.org/W2313094819","https://openalex.org/W2477765720","https://openalex.org/W2481066337","https://openalex.org/W2500022925","https://openalex.org/W2519132385","https://openalex.org/W2524721548","https://openalex.org/W2582743722","https://openalex.org/W2796514099","https://openalex.org/W4205428221","https://openalex.org/W4205687621","https://openalex.org/W4234536190","https://openalex.org/W4235169531","https://openalex.org/W4244055225","https://openalex.org/W4298364955","https://openalex.org/W6723265790"],"related_works":["https://openalex.org/W4241767317","https://openalex.org/W2188840951","https://openalex.org/W4220814143","https://openalex.org/W2085801246","https://openalex.org/W3092684173","https://openalex.org/W2770741777","https://openalex.org/W2040929534","https://openalex.org/W2183813428","https://openalex.org/W2101637161","https://openalex.org/W2101887416"],"abstract_inverted_index":{"Many":[0],"clustering":[1,54,86],"methods":[2],"perform":[3],"well":[4],"with":[5,10,81],"spherical":[6],"clusters":[7,109,115],"but":[8,25],"poorly":[9],"elongated":[11,72],"clusters.":[12,73],"The":[13,117],"Single-linkage":[14],"method":[15],"is":[16,66,97],"suitable":[17],"for":[18,68,101,131],"finding":[19],"such":[20],"type":[21],"of":[22,46,60,71,84,104,108,111,114],"long":[23],"clusters,":[24,61],"it":[26,80],"can":[27,128],"be":[28,129],"sensitive":[29],"to":[30],"outliers":[31,63],"and":[32,64,92,110],"noise":[33],"in":[34,122],"the":[35,38,49,58,69,102,105,112,123,134],"data,":[36],"causing":[37],"so-called":[39],"chain-effect.":[40],"This":[41],"work":[42],"proposes":[43],"a":[44,52,82,98],"modification":[45],"Cross-Clustering":[47],"algorithm,":[48],"SingleCross-Clustering":[50],"(SCC),":[51],"partial":[53],"algorithm":[55,118],"that":[56,65,95],"estimates":[57],"number":[59,83,107,113],"recognizes":[62],"useful":[67],"identification":[70,103],"SCC":[74,96],"has":[75,119],"been":[76,120],"validated":[77],"by":[78],"comparing":[79],"existing":[85],"methods,":[87],"showing":[88],"on":[89],"both":[90],"simulated":[91],"real":[93],"datasets":[94],"reliable":[99],"solution":[100],"correct":[106],"memberships.":[116],"implemented":[121],"R":[124],"package":[125,137],"CrossClustering,":[126],"which":[127],"downloaded":[130],"free":[132],"from":[133],"CRAN":[135],"contributed":[136],"repository.":[138]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}