{"id":"https://openalex.org/W2778728785","doi":"https://doi.org/10.5430/air.v7n1p15","title":"Estimating the number of clusters using diversity","display_name":"Estimating the number of clusters using diversity","publication_year":2017,"publication_date":"2017-12-18","ids":{"openalex":"https://openalex.org/W2778728785","doi":"https://doi.org/10.5430/air.v7n1p15","mag":"2778728785"},"language":"en","primary_location":{"id":"doi:10.5430/air.v7n1p15","is_oa":true,"landing_page_url":"https://doi.org/10.5430/air.v7n1p15","pdf_url":"http://www.sciedupress.com/journal/index.php/air/article/download/12360/7881","source":{"id":"https://openalex.org/S4210167461","display_name":"Artificial Intelligence Research","issn_l":"1927-6974","issn":["1927-6974","1927-6982"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320842","host_organization_name":"Sciedu Press","host_organization_lineage":["https://openalex.org/P4310320842"],"host_organization_lineage_names":["Sciedu Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Research","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"http://www.sciedupress.com/journal/index.php/air/article/download/12360/7881","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054642388","display_name":"Suneel Kumar Kingrani","orcid":null},"institutions":[{"id":"https://openalex.org/I98259816","display_name":"Birkbeck, University of London","ror":"https://ror.org/02mb95055","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I98259816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Suneel Kumar Kingrani","raw_affiliation_strings":["Birkbeck, University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Birkbeck, University of London","institution_ids":["https://openalex.org/I98259816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015725705","display_name":"Mark Levene","orcid":"https://orcid.org/0000-0001-8632-4732"},"institutions":[{"id":"https://openalex.org/I98259816","display_name":"Birkbeck, University of London","ror":"https://ror.org/02mb95055","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I98259816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mark Levene","raw_affiliation_strings":["Birkbeck, University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Birkbeck, University of London","institution_ids":["https://openalex.org/I98259816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015784640","display_name":"Dell Zhang","orcid":"https://orcid.org/0000-0002-8774-3725"},"institutions":[{"id":"https://openalex.org/I98259816","display_name":"Birkbeck, University of London","ror":"https://ror.org/02mb95055","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I98259816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Dell Zhang","raw_affiliation_strings":["Birkbeck, University of London"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Birkbeck, University of London","institution_ids":["https://openalex.org/I98259816"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8915,"has_fulltext":true,"cited_by_count":51,"citation_normalized_percentile":{"value":0.93108552,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"7","issue":"1","first_page":"15","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9968000054359436,"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":0.9968000054359436,"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":0.9919000267982483,"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"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9904999732971191,"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/cluster-analysis","display_name":"Cluster analysis","score":0.6831499338150024},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5411843657493591},{"id":"https://openalex.org/keywords/silhouette","display_name":"Silhouette","score":0.5270500183105469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5199781060218811},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5052149891853333},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4848114550113678},{"id":"https://openalex.org/keywords/single-linkage-clustering","display_name":"Single-linkage clustering","score":0.47148844599723816},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.467048317193985},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4495960474014282},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4316681921482086},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.42460185289382935},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.417610764503479},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33641430735588074},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.32262498140335083},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2402588129043579},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.16187193989753723},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0770198404788971}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6831499338150024},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5411843657493591},{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.5270500183105469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5199781060218811},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5052149891853333},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4848114550113678},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.47148844599723816},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.467048317193985},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4495960474014282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4316681921482086},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.42460185289382935},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.417610764503479},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33641430735588074},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.32262498140335083},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2402588129043579},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.16187193989753723},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0770198404788971},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5430/air.v7n1p15","is_oa":true,"landing_page_url":"https://doi.org/10.5430/air.v7n1p15","pdf_url":"http://www.sciedupress.com/journal/index.php/air/article/download/12360/7881","source":{"id":"https://openalex.org/S4210167461","display_name":"Artificial Intelligence Research","issn_l":"1927-6974","issn":["1927-6974","1927-6982"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320842","host_organization_name":"Sciedu Press","host_organization_lineage":["https://openalex.org/P4310320842"],"host_organization_lineage_names":["Sciedu Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Research","raw_type":"journal-article"},{"id":"pmh:oai:eprints.bbk.ac.uk.oai2:20714","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400466","display_name":"BIROn (Birkbeck, University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98259816","host_organization_name":"Birkbeck, University of London","host_organization_lineage":["https://openalex.org/I98259816"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":{"id":"doi:10.5430/air.v7n1p15","is_oa":true,"landing_page_url":"https://doi.org/10.5430/air.v7n1p15","pdf_url":"http://www.sciedupress.com/journal/index.php/air/article/download/12360/7881","source":{"id":"https://openalex.org/S4210167461","display_name":"Artificial Intelligence Research","issn_l":"1927-6974","issn":["1927-6974","1927-6982"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320842","host_organization_name":"Sciedu Press","host_organization_lineage":["https://openalex.org/P4310320842"],"host_organization_lineage_names":["Sciedu Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Artificial Intelligence Research","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2778728785.pdf","grobid_xml":"https://content.openalex.org/works/W2778728785.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W657581592","https://openalex.org/W1518829424","https://openalex.org/W1523767002","https://openalex.org/W1535743328","https://openalex.org/W1653902796","https://openalex.org/W1673310716","https://openalex.org/W1975152892","https://openalex.org/W1978627835","https://openalex.org/W1980849679","https://openalex.org/W1996881001","https://openalex.org/W2000841954","https://openalex.org/W2011430131","https://openalex.org/W2028602070","https://openalex.org/W2071949631","https://openalex.org/W2098515641","https://openalex.org/W2104767862","https://openalex.org/W2110158660","https://openalex.org/W2120474334","https://openalex.org/W2129802470","https://openalex.org/W2132314908","https://openalex.org/W2134199473","https://openalex.org/W2165232124","https://openalex.org/W2165874743","https://openalex.org/W2168069606","https://openalex.org/W2171033594","https://openalex.org/W2460884160","https://openalex.org/W2474047286","https://openalex.org/W2505595263","https://openalex.org/W2595840341","https://openalex.org/W3120740533","https://openalex.org/W4206671592","https://openalex.org/W4213009331","https://openalex.org/W4242401062","https://openalex.org/W4245799227","https://openalex.org/W4292734943"],"related_works":["https://openalex.org/W2129417512","https://openalex.org/W2599570117","https://openalex.org/W3087769312","https://openalex.org/W4231226332","https://openalex.org/W2071977683","https://openalex.org/W2406185607","https://openalex.org/W4230135178","https://openalex.org/W4250422795","https://openalex.org/W2532285747","https://openalex.org/W2181546347"],"abstract_inverted_index":{"It":[0],"is":[1,24,90,103,127,157,169],"an":[2,76],"important":[3],"and":[4,61,118,144,165,167,179],"challenging":[5],"problem":[6],"in":[7,16,181],"unsupervised":[8],"learning":[9],"to":[10,46,130],"estimate":[11],"the":[12,20,53,56,62,80,83,88,114,119,186],"number":[13,21,84,188],"of":[14,22,59,64,69,79,82,85,99,116,161,183,189],"clusters":[15,23,60,117,134,160],"a":[17,25,41],"dataset.":[18],"Knowing":[19],"prerequisite":[26],"for":[27,159],"many":[28],"commonly":[29],"used":[30,74],"clustering":[31,108],"algorithms":[32],"such":[33],"as":[34,75],"\\textit{k}-means.":[35],"In":[36,123],"this":[37,47],"paper,":[38],"we":[39,50],"propose":[40],"novel":[42],"diversity":[43,58,68,89],"based":[44],"approach":[45],"problem.":[48],"Specifically,":[49],"show":[51],"that":[52,104,153],"difference":[54],"between":[55,121],"global":[57],"sum":[63],"each":[65],"cluster\u2019s":[66],"local":[67],"their":[70],"members":[71],"can":[72],"be":[73],"effective":[77],"indicator":[78],"optimality":[81],"clusters,":[86],"where":[87],"measured":[91],"by":[92,109],"Rao\u2019s":[93],"quadratic":[94],"entropy.":[95],"A":[96],"notable":[97],"advantage":[98],"our":[100,154],"proposed":[101,155],"method":[102,156],"it":[105,126,168],"encourages":[106],"balanced":[107],"taking":[110],"into":[111],"account":[112],"both":[113,142],"sizes":[115],"distances":[120],"clusters.":[122,190],"other":[124],"words,":[125],"less":[128],"prone":[129],"very":[131],"small":[132],"\u201coutlier\u201d":[133],"than":[135,172],"existing":[136,173],"methods.":[137],"Our":[138],"extensive":[139],"experiments":[140],"on":[141],"synthetic":[143],"real-world":[145],"datasets":[146],"(with":[147],"known":[148],"ground-truth":[149],"clustering)":[150],"have":[151],"demonstrated":[152],"robust":[158],"different":[162],"sizes,":[163],"variances,":[164],"shapes,":[166],"more":[170],"accurate":[171],"methods":[174],"(including":[175],"elbow,":[176],"Cali\u0144ski-Harabasz,":[177],"silhouette,":[178],"gap-statistic)":[180],"terms":[182],"finding":[184],"out":[185],"optimal":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
