{"id":"https://openalex.org/W2736104978","doi":"https://doi.org/10.1109/icnidc.2016.7974565","title":"Revealing functional regions via joint matrix factorization based model","display_name":"Revealing functional regions via joint matrix factorization based model","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2736104978","doi":"https://doi.org/10.1109/icnidc.2016.7974565","mag":"2736104978"},"language":"en","primary_location":{"id":"doi:10.1109/icnidc.2016.7974565","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnidc.2016.7974565","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)","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/A5101903923","display_name":"Shan Wang","orcid":"https://orcid.org/0000-0001-6243-956X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shan Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101034795","display_name":"Yajing Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajing Xu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101707899","display_name":"Sheng Gao","orcid":"https://orcid.org/0000-0003-1591-0595"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Gao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"205","last_page":"209"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":1.0,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9836000204086304,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9763000011444092,"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/computer-science","display_name":"Computer science","score":0.7418137788772583},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6574860215187073},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.5874994397163391},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5612455606460571},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5487712621688843},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5477404594421387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5258313417434692},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5047167539596558},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4984304904937744},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4970448315143585},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.472368061542511},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4512535333633423},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43300721049308777},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.4297572374343872},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15163248777389526},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10896411538124084}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7418137788772583},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6574860215187073},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.5874994397163391},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5612455606460571},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5487712621688843},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5477404594421387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5258313417434692},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5047167539596558},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4984304904937744},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4970448315143585},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.472368061542511},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4512535333633423},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43300721049308777},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.4297572374343872},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15163248777389526},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10896411538124084},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnidc.2016.7974565","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnidc.2016.7974565","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1513719591","https://openalex.org/W2019786737","https://openalex.org/W2082729958","https://openalex.org/W2101823987","https://openalex.org/W2106403424","https://openalex.org/W2153207204","https://openalex.org/W2153458569","https://openalex.org/W2330828539","https://openalex.org/W3143596294"],"related_works":["https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W34555840","https://openalex.org/W2146544734"],"abstract_inverted_index":{"Various":[0],"functions":[1,35],"in":[2],"the":[3,8,14,25,33,37,53,65,73,100,105,113,117,124,128,134,140,145,152],"region":[4,34],"are":[5],"emerging":[6],"with":[7,36],"process":[9,126],"of":[10,108,136,147],"urbanizations,":[11],"such":[12],"as":[13],"residential,":[15],"entertainment":[16],"or":[17,60],"hospital":[18],"districts,":[19],"which":[20],"can":[21,119],"be":[22,120],"indicated":[23],"during":[24],"urban":[26,110],"planning.":[27],"Recently,":[28],"researchers":[29],"try":[30],"to":[31,63,98,143],"discover":[32],"human":[38,80],"mobility":[39,58],"data":[40,56,102],"based":[41,122],"on":[42,123,139],"machine":[43],"learning":[44],"and":[45,85,103],"statistical":[46],"models.":[47],"However,":[48],"previous":[49],"work":[50],"always":[51],"employs":[52],"single":[54],"domain":[55],"like":[57,79],"information":[59,84],"district":[61],"attributes":[62],"measure":[64],"functions.":[66],"In":[67],"this":[68],"paper,":[69],"we":[70,90],"will":[71],"address":[72],"problem":[74],"by":[75],"integrating":[76],"multi-domain":[77],"data,":[78],"trajectories,":[81],"base":[82],"station":[83],"Points-of-Interest":[86],"attributes.":[87],"For":[88],"that,":[89],"propose":[91],"a":[92],"joint":[93],"nonnegative":[94],"matrix":[95],"factorization":[96],"model":[97,150],"combine":[99],"multi-source":[101],"extract":[104],"function":[106,115],"distribution":[107],"each":[109],"region,":[111],"then":[112],"dominated":[114],"among":[116],"regions":[118],"uncovered":[121],"clustering":[125],"over":[127,151],"extracted":[129],"distributions.":[130],"We":[131],"also":[132],"evaluate":[133],"performance":[135],"our":[137,148],"method":[138],"real-world":[141],"dataset":[142],"demonstrate":[144],"advantages":[146],"proposed":[149],"baseline":[153],"methods.":[154]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}