{"id":"https://openalex.org/W2127258367","doi":"https://doi.org/10.1109/icdm.2002.1183901","title":"A parameterless method for efficiently discovering clusters of arbitrary shape in large datasets","display_name":"A parameterless method for efficiently discovering clusters of arbitrary shape in large datasets","publication_year":2003,"publication_date":"2003-06-26","ids":{"openalex":"https://openalex.org/W2127258367","doi":"https://doi.org/10.1109/icdm.2002.1183901","mag":"2127258367"},"language":"en","primary_location":{"id":"doi:10.1109/icdm.2002.1183901","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2002.1183901","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","raw_type":"proceedings-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/A5084361597","display_name":"Andrew Foss","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"A. Foss","raw_affiliation_strings":["University of Alberta, Canada","Alberta Univ., Edmonton, Alta., Canada"],"affiliations":[{"raw_affiliation_string":"University of Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]},{"raw_affiliation_string":"Alberta Univ., Edmonton, Alta., Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053745515","display_name":"Osmar R. Za\u0131\u0308ane","orcid":"https://orcid.org/0000-0002-0060-5988"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"O.R. Zaiane","raw_affiliation_strings":["University of Alberta, Canada","Alberta Univ., Edmonton, Alta., Canada"],"affiliations":[{"raw_affiliation_string":"University of Alberta, Canada","institution_ids":["https://openalex.org/I154425047"]},{"raw_affiliation_string":"Alberta Univ., Edmonton, Alta., Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084361597"],"corresponding_institution_ids":["https://openalex.org/I154425047"],"apc_list":null,"apc_paid":null,"fwci":6.0835,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.96326203,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"179","last_page":"186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9998999834060669,"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":0.9998999834060669,"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/T11106","display_name":"Data Management and Algorithms","score":0.9983999729156494,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9105497002601624},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6921650767326355},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.6755286455154419},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.6685766577720642},{"id":"https://openalex.org/keywords/sort","display_name":"sort","score":0.6390612125396729},{"id":"https://openalex.org/keywords/single-linkage-clustering","display_name":"Single-linkage clustering","score":0.5890913605690002},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5739841461181641},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.565732479095459},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.5334627032279968},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5318683385848999},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.47950536012649536},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4628436267375946},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.46272507309913635},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4473872184753418},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43957701325416565},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.06093665957450867}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.9105497002601624},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6921650767326355},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.6755286455154419},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.6685766577720642},{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.6390612125396729},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.5890913605690002},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5739841461181641},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.565732479095459},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.5334627032279968},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5318683385848999},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.47950536012649536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4628436267375946},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.46272507309913635},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4473872184753418},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43957701325416565},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.06093665957450867},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icdm.2002.1183901","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2002.1183901","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.7.1966","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.1966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.ualberta.ca/~zaiane/postscript/icdm02-2.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1633659671","https://openalex.org/W1673310716","https://openalex.org/W1971784203","https://openalex.org/W1977496278","https://openalex.org/W1983175601","https://openalex.org/W2057712948","https://openalex.org/W2088698696","https://openalex.org/W2099581008","https://openalex.org/W2129066856","https://openalex.org/W2131687179","https://openalex.org/W2140190241","https://openalex.org/W2141585940","https://openalex.org/W2160642098","https://openalex.org/W2319660501","https://openalex.org/W4247105055","https://openalex.org/W6636873521","https://openalex.org/W6637131181","https://openalex.org/W6770641979"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W2160785859","https://openalex.org/W2188840951","https://openalex.org/W3120229345","https://openalex.org/W2374506950","https://openalex.org/W2171610853","https://openalex.org/W1999117613","https://openalex.org/W3140018618","https://openalex.org/W2770741777","https://openalex.org/W2393707058"],"abstract_inverted_index":{"Clustering":[0],"is":[1,28],"the":[2,14,19,50,55,59],"problem":[3,23],"of":[4,12,61,68,89],"grouping":[5],"data":[6,26,119],"based":[7],"on":[8],"similarity":[9,16],"and":[10,80,91,115],"consists":[11],"maximizing":[13],"intra-group":[15],"while":[17],"minimizing":[18],"inter-group":[20],"similarity.":[21],"The":[22],"Of":[24],"clustering":[25,43,51,82,96,111],"sets":[27],"also":[29],"known":[30],"as":[31,54],"unsupervised":[32],"classification,":[33],"since":[34],"no":[35],"class":[36],"labels":[37],"are":[38],"given.":[39],"However,":[40],"all":[41],"existing":[42,110],"algorithms":[44,112],"require":[45],"some":[46],"parameters":[47],"to":[48],"steer":[49],"process,":[52],"such":[53],"famous":[56],"k":[57],"for":[58,117],"number":[60],"expected":[62],"clusters,":[63],"which":[64],"constitutes":[65],"a":[66,69,76,87,93],"supervision":[67],"sort.":[70],"We":[71],"present":[72],"in":[73,113],"this":[74],"paper":[75],"new,":[77],"efficient,":[78],"fast":[79],"scalable":[81],"algorithm":[83,107],"that":[84,105],"clusters":[85],"over":[86],"range":[88],"resolutions":[90],"finds":[92],"potential":[94],"optimum":[95],"without":[97],"requiring":[98],"any":[99],"parameter":[100],"input.":[101],"Our":[102],"experiments":[103],"show":[104],"our":[106],"outperforms":[108],"most":[109],"quality":[114],"speed":[116],"large":[118],"sets.":[120]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":4}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
