{"id":"https://openalex.org/W7123576094","doi":"https://doi.org/10.1142/s021800142651002x","title":"Unsupervised Learning on Stream Data: Clusterability Analysis in a Joint Perspective Under Incremental and Parallel Constraints","display_name":"Unsupervised Learning on Stream Data: Clusterability Analysis in a Joint Perspective Under Incremental and Parallel Constraints","publication_year":2026,"publication_date":"2026-01-14","ids":{"openalex":"https://openalex.org/W7123576094","doi":"https://doi.org/10.1142/s021800142651002x"},"language":"en","primary_location":{"id":"doi:10.1142/s021800142651002x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021800142651002x","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","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":null,"display_name":"Chunlei Chen","orcid":"https://orcid.org/0000-0003-0883-0159"},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunlei Chen","raw_affiliation_strings":["School of Computer Engineering, Weifang University, Weifang, P. R. China"],"raw_orcid":"https://orcid.org/0000-0003-0883-0159","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Weifang University, Weifang, P. R. China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103848506","display_name":"Jinkui Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinkui Hou","raw_affiliation_strings":["School of Computer Engineering, Weifang University, Weifang, P. R. China"],"raw_orcid":"https://orcid.org/0009-0001-6548-3299","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Weifang University, Weifang, P. R. China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiangyan Dai","orcid":"https://orcid.org/0000-0003-1695-4629"},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangyan Dai","raw_affiliation_strings":["School of Computer Engineering, Weifang University, Weifang, P. R. China"],"raw_orcid":"https://orcid.org/0000-0003-1695-4629","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Weifang University, Weifang, P. R. China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122981639","display_name":"Huihui Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huihui Zhang","raw_affiliation_strings":["School of Computer Engineering, Weifang University, Weifang, P. R. China"],"raw_orcid":"https://orcid.org/0000-0002-1012-8089","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Weifang University, Weifang, P. R. China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122934413","display_name":"Guoxu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoxu Liu","raw_affiliation_strings":["School of Computer Engineering, Weifang University, Weifang, P. R. China"],"raw_orcid":"https://orcid.org/0000-0002-3668-4569","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Weifang University, Weifang, P. R. China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122950381","display_name":"Yujie Li","orcid":null},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujie Li","raw_affiliation_strings":["School of Computer Engineering, Weifang University, Weifang, P. R. China"],"raw_orcid":"https://orcid.org/0009-0001-8527-9041","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Weifang University, Weifang, P. R. China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lu Hong","orcid":"https://orcid.org/0009-0002-5155-5762"},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Hong","raw_affiliation_strings":["School of Computer Engineering, Weifang University, Weifang, P. R. China"],"raw_orcid":"https://orcid.org/0009-0002-5155-5762","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Weifang University, Weifang, P. R. China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122953852","display_name":"Jia Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136047","display_name":"Weifang University of Science and Technology","ror":"https://ror.org/04ha2bb10","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136047"]},{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Liu","raw_affiliation_strings":["School of Big Data, Weifang Institute of Technology, Weifang, P. R. China"],"raw_orcid":"https://orcid.org/0000-0002-8942-5392","affiliations":[{"raw_affiliation_string":"School of Big Data, Weifang Institute of Technology, Weifang, P. R. China","institution_ids":["https://openalex.org/I4210136047","https://openalex.org/I66883779"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05788338,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"06","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.47679999470710754,"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":0.47679999470710754,"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":0.17509999871253967,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.05389999970793724,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8565000295639038},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.6114000082015991},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.599399983882904},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5949000120162964},{"id":"https://openalex.org/keywords/conceptual-clustering","display_name":"Conceptual clustering","score":0.5311999917030334},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.5246000289916992},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.4505000114440918},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.42829999327659607},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.41019999980926514}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8565000295639038},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940000295639038},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.6114000082015991},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5949000120162964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5666000247001648},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.5311999917030334},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5157999992370605},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.4505000114440918},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43790000677108765},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.40790000557899475},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.3986999988555908},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.39649999141693115},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.3555000126361847},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.3050000071525574},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.2791999876499176},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.2623000144958496}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s021800142651002x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021800142651002x","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3315838394","display_name":null,"funder_award_id":"M2024314","funder_id":"https://openalex.org/F4320313896","funder_display_name":"Department of Education of Shandong Province"},{"id":"https://openalex.org/G6119919276","display_name":null,"funder_award_id":"ZR2021MF085","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G7602884701","display_name":null,"funder_award_id":"J17KB183","funder_id":"https://openalex.org/F4320313896","funder_display_name":"Department of Education of Shandong Province"},{"id":"https://openalex.org/G805162624","display_name":null,"funder_award_id":"ZR2023QC116","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"}],"funders":[{"id":"https://openalex.org/F4320313896","display_name":"Department of Education of Shandong Province","ror":"https://ror.org/01ksk4k18"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2007470648","https://openalex.org/W2171394996","https://openalex.org/W2785648173","https://openalex.org/W2976670629","https://openalex.org/W3126658104","https://openalex.org/W3205833277","https://openalex.org/W4213418104","https://openalex.org/W4310367455","https://openalex.org/W4311756738","https://openalex.org/W4312208416","https://openalex.org/W4319662873","https://openalex.org/W4322725973","https://openalex.org/W4362474940","https://openalex.org/W4366003831","https://openalex.org/W4367047043","https://openalex.org/W4382935781","https://openalex.org/W4383375622","https://openalex.org/W4384661698","https://openalex.org/W4386085871","https://openalex.org/W4390856178","https://openalex.org/W4393309113","https://openalex.org/W4402448414","https://openalex.org/W4403465936","https://openalex.org/W4410141156","https://openalex.org/W4410341815","https://openalex.org/W7110271608"],"related_works":[],"abstract_inverted_index":{"Unsupervised":[0],"learning":[1,8,15],"is":[2,11,31,79,89,139,235,249,297,370],"one":[3],"of":[4,24,59,111,119,131,155,174,186,206,257,294,300,312,331,347,358,367],"the":[5,22,37,44,57,72,76,94,107,112,120,132,152,198,232,240,245,260,292,298,329,345,356,364],"fundamental":[6],"machine":[7],"methods.":[9],"Clustering":[10],"a":[12,48,53,100,128,207,221,255,288,310,348,359,377],"vital":[13,289],"unsupervised":[14],"task":[16],"and":[17,103,196,230,259,335,385],"can":[18,41,51,104,319,327,350],"significantly":[19],"contribute":[20],"to":[21,36,46,71,81,142,193,237,251,307,379],"detection":[23],"hidden":[25],"structures":[26],"in":[27,99,127,204,239,344,355],"unknown":[28],"datasets.":[29],"Clusterability":[30],"an":[32,175,201,272],"important":[33],"concept":[34],"due":[35],"fact":[38],"that":[39,226,276,287,318,342],"it":[40],"theoretically":[42,194],"portray":[43],"extent":[45],"which":[47,179],"clustering":[49,77,96,138,177,223,225],"algorithm":[50,78,97],"recover":[52],"benchmark":[54,134,323],"clustering,":[55],"with":[56],"absence":[58],"excessive":[60],"experimental":[61],"validations.":[62],"Moreover,":[63],"conventional":[64],"batch-mode-clustering-oriented":[65],"clusterability":[66,87,199,209,381],"analysis":[67,88],"should":[68],"be":[69,252],"extended":[70],"incremental":[73,95,137,176,202,384],"setting":[74,203],"when":[75],"required":[80],"handle":[82],"stream":[83,124,248],"data.":[84,282],"However,":[85,164],"such":[86],"facing":[90],"two":[91,171],"barriers.":[92],"First,":[93,283],"proceeds":[98],"step-wise":[101],"manner":[102],"merely":[105,279],"access":[106],"newly":[108],"arrived":[109],"data":[110,123,166,182,242,247,361],"current":[113],"step.":[114],"This":[115],"extremely":[116],"fragmentary":[117],"view":[118,185],"entire":[121],"input":[122,241,246],"inevitably":[125],"results":[126],"biased":[129],"perception":[130],"underlying":[133],"clustering.":[135],"Second,":[136,339],"conventionally":[140],"applied":[141],"real-time":[143],"or":[144],"massive-data":[145],"scenarios.":[146],"Such":[147],"application":[148],"scenarios":[149],"typically":[150],"require":[151],"computational":[153],"power":[154],"mainstream":[156],"SIMD":[157],"(Single":[158],"Instruction":[159],"Multiple":[160],"Data)":[161],"hardware":[162],"accelerators.":[163],"strong":[165],"dependency":[167],"inherently":[168],"exists":[169],"between":[170],"successive":[172],"steps":[173],"algorithm,":[178],"dramatically":[180],"impairs":[181],"parallelism.":[183],"In":[184,218,267,373],"these":[187],"constraints,":[188],"we":[189,269,284,326,340,375],"propose":[190,304],"our":[191,219],"roadmap":[192,378],"analyze":[195],"ensure":[197],"under":[200,382],"terms":[205],"general":[208],"metric:":[210],"niceness":[211,233],"(higher":[212],"intra-cluster":[213],"similarity":[214],"than":[215,354],"inter-cluster":[216],"similarity).":[217],"work,":[220],"nice-k":[222],"(a":[224],"has":[227],"k":[228,322],"clusters":[229,296,332],"satisfies":[231],"metric)":[234],"supposed":[236,250],"exist":[238],"stream.":[243],"Meanwhile,":[244],"divided":[253],"into":[254,265],"series":[256],"micro-clusters,":[258],"micro-clusters":[261,317],"are":[262],"incrementally":[263],"clustered":[264],"clusters.":[266,324],"addition,":[268],"rely":[270],"on":[271],"assumption":[273],"(homogeneity":[274],"assumption)":[275],"every":[277],"micro-cluster":[278,349],"contains":[280],"homogenous":[281],"point":[285],"out":[286],"reason":[290],"for":[291,333],"induction":[293],"heterogeneous":[295],"lack":[299],"representative":[301,316],"micro-clusters.":[302],"We":[303],"Theorem":[305,368],"1":[306,369],"iteratively":[308],"identify":[309],"set":[311],"2[Formula:":[313],"see":[314],"text]":[315],"cover":[320],"all":[321],"Therefore,":[325],"trade":[328],"number":[330],"homogeneity":[334],"thus":[336],"assure":[337,380],"clusterability.":[338],"demonstrate":[341],"evolution":[343],"granularity":[346,357],"prompt":[351],"SIMD-parallelism":[352],"more":[353],"single":[360],"point.":[362],"Consequently,":[363],"clusterability-assured":[365],"method":[366],"furthermore":[371],"parallel-friendly.":[372],"all,":[374],"depict":[376],"both":[383],"SIMD-friendly":[386],"constraints.":[387]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2026-01-14T00:00:00"}