{"id":"https://openalex.org/W2092335550","doi":"https://doi.org/10.1145/1281192.1281210","title":"Density-based clustering for real-time stream data","display_name":"Density-based clustering for real-time stream data","publication_year":2007,"publication_date":"2007-08-12","ids":{"openalex":"https://openalex.org/W2092335550","doi":"https://doi.org/10.1145/1281192.1281210","mag":"2092335550"},"language":"en","primary_location":{"id":"doi:10.1145/1281192.1281210","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1281192.1281210","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5100393445","display_name":"Yixin Chen","orcid":"https://orcid.org/0000-0002-3704-4432"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yixin Chen","raw_affiliation_strings":["Washington University in St. Louis","(, Washington University in St. Louis)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington University in St. Louis","institution_ids":["https://openalex.org/I204465549"]},{"raw_affiliation_string":"(, Washington University in St. Louis)","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101548392","display_name":"Li Tu","orcid":"https://orcid.org/0000-0003-3284-4083"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Tu","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.793,"has_fulltext":false,"cited_by_count":565,"citation_normalized_percentile":{"value":0.98884681,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"133","last_page":"142"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","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/T12761","display_name":"Data Stream Mining Techniques","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":0.9987000226974487,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.8653978109359741},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8399621844291687},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7872012257575989},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.696806013584137},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6460404992103577},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.5976223349571228},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.563922107219696},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.543803870677948},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.49823451042175293},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.46716463565826416},{"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":0.4562261700630188},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.45098504424095154},{"id":"https://openalex.org/keywords/dbscan","display_name":"DBSCAN","score":0.43255677819252014},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3882772922515869},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2153720259666443},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13900110125541687}],"concepts":[{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.8653978109359741},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8399621844291687},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7872012257575989},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.696806013584137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6460404992103577},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.5976223349571228},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.563922107219696},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.543803870677948},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.49823451042175293},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.46716463565826416},{"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":0.4562261700630188},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.45098504424095154},{"id":"https://openalex.org/C46576248","wikidata":"https://www.wikidata.org/wiki/Q1114630","display_name":"DBSCAN","level":5,"score":0.43255677819252014},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3882772922515869},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2153720259666443},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13900110125541687},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1281192.1281210","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1281192.1281210","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.98.2979","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.98.2979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.wustl.edu/~chen/public/sigproc-sp.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/W1564015926","https://openalex.org/W1949687736","https://openalex.org/W1965456679","https://openalex.org/W1976492765","https://openalex.org/W1998783890","https://openalex.org/W2004787755","https://openalex.org/W2035959592","https://openalex.org/W2100369465","https://openalex.org/W2104798029","https://openalex.org/W2110061186","https://openalex.org/W2123297508","https://openalex.org/W2124507579","https://openalex.org/W2126751256","https://openalex.org/W2132851185","https://openalex.org/W2144846860","https://openalex.org/W2153610999","https://openalex.org/W2169581671","https://openalex.org/W2170936641"],"related_works":["https://openalex.org/W2054669762","https://openalex.org/W2014841791","https://openalex.org/W2353158678","https://openalex.org/W2394193399","https://openalex.org/W2998469710","https://openalex.org/W2809859387","https://openalex.org/W2392255773","https://openalex.org/W2387373525","https://openalex.org/W2547681860","https://openalex.org/W2355615404"],"abstract_inverted_index":{"Existing":[0],"data-stream":[1],"clustering":[2,11,46,163,168],"algorithms":[3,12],"such":[4],"as":[5],"CluStream":[6],"arebased":[7],"on":[8,81],"k-means.":[9],"These":[10],"are":[13],"incompetent":[14],"tofind":[15],"clusters":[16,77,123,184],"of":[17,29,96,154,185,195],"arbitrary":[18,186],"shapes":[19],"and":[20,31,67,76,110,117,120,136,151,180,188],"cannot":[21],"handle":[22],"outliers.":[23],"Further,":[24,127],"they":[25],"require":[26],"the":[27,73,78,82,93,101,105,122,149,155,167,192],"knowledge":[28],"k":[30],"user-specified":[32],"time":[33,152],"window.":[34],"To":[35],"address":[36],"these":[37],"issues,":[38],"this":[39],"paper":[40],"proposes":[41],"D-Stream,":[42],"a":[43,65,87,97,128],"framework":[44],"for":[45],"stream":[47,162],"data":[48,62,98,108,161,197],"using":[49],"adensity-based":[50],"approach.":[51],"The":[52,84,157,170],"algorithm":[53,85,114,176],"uses":[54],"an":[55,68],"online":[56],"component":[57,70],"which":[58,71],"maps":[59],"each":[60],"input":[61],"record":[63],"into":[64],"grid":[66,74],"offline":[69],"computes":[72],"density":[75,88,109],"grids":[79,139],"based":[80],"density.":[83],"adopts":[86],"decaying":[89],"technique":[90,131,158],"to":[91,134,141,146],"capture":[92],"dynamic":[94],"changes":[95],"stream.":[99],"Exploiting":[100],"intricate":[102],"relationships":[103],"between":[104],"decay":[106],"factor,":[107],"cluster":[111],"structure,":[112],"our":[113,175],"can":[115,182,189],"efficiently":[116],"effectively":[118],"generate":[119],"adjust":[121],"in":[124,144],"real":[125],"time.":[126],"theoretically":[129],"sound":[130],"is":[132],"developed":[133],"detect":[135],"remove":[137],"sporadic":[138],"mapped":[140],"by":[142],"outliers":[143],"order":[145],"dramatically":[147],"improve":[148],"space":[150],"efficiency":[153],"system.":[156],"makes":[159],"high-speed":[160],"feasible":[164],"without":[165],"degrading":[166],"quality.":[169],"experimental":[171],"results":[172],"show":[173],"that":[174],"has":[177],"superior":[178],"quality":[179],"efficiency,":[181],"find":[183],"shapes,":[187],"accurately":[190],"recognize":[191],"evolving":[193],"behaviors":[194],"real-time":[196],"streams.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":35},{"year":2021,"cited_by_count":32},{"year":2020,"cited_by_count":33},{"year":2019,"cited_by_count":55},{"year":2018,"cited_by_count":58},{"year":2017,"cited_by_count":37},{"year":2016,"cited_by_count":43},{"year":2015,"cited_by_count":30},{"year":2014,"cited_by_count":31},{"year":2013,"cited_by_count":42},{"year":2012,"cited_by_count":30}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}