{"id":"https://openalex.org/W2467602798","doi":"https://doi.org/10.1109/noms.2016.7502969","title":"Clustering-based KPI data association analysis method in cellular networks","display_name":"Clustering-based KPI data association analysis method in cellular networks","publication_year":2016,"publication_date":"2016-04-01","ids":{"openalex":"https://openalex.org/W2467602798","doi":"https://doi.org/10.1109/noms.2016.7502969","mag":"2467602798"},"language":"en","primary_location":{"id":"doi:10.1109/noms.2016.7502969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/noms.2016.7502969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium","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/A5075734606","display_name":"Xingyu Guo","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":true,"raw_author_name":"Xingyu Guo","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100783073","display_name":"Peng Yu","orcid":"https://orcid.org/0000-0002-0402-5390"},"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":"Peng Yu","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442689","display_name":"Wenjing Li","orcid":"https://orcid.org/0000-0003-3852-1007"},"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":"Wenjing Li","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042944721","display_name":"Xuesong Qiu","orcid":"https://orcid.org/0000-0002-7899-539X"},"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":"Xuesong Qiu","raw_affiliation_strings":["State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, P. R. China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075734606"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.8839,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75245175,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"96","issue":null,"first_page":"1101","last_page":"1104"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9923999905586243,"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"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9923999905586243,"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"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9837999939918518,"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/T12384","display_name":"Customer churn and segmentation","score":0.9726999998092651,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/performance-indicator","display_name":"Performance indicator","score":0.7782312035560608},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7329025864601135},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.725854754447937},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6154831051826477},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.47286248207092285},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2636529207229614}],"concepts":[{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.7782312035560608},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7329025864601135},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.725854754447937},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6154831051826477},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.47286248207092285},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2636529207229614},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/noms.2016.7502969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/noms.2016.7502969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1484918731","https://openalex.org/W1673310716","https://openalex.org/W1966292371","https://openalex.org/W2018121109","https://openalex.org/W2086621998","https://openalex.org/W2090754752","https://openalex.org/W2151854870","https://openalex.org/W2165835468","https://openalex.org/W6637131181"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"With":[0],"the":[1,8,38,60,87,101],"rapid":[2],"development":[3],"of":[4,23],"cellular":[5],"network":[6,17,83],"systems,":[7],"operators":[9],"need":[10],"more":[11],"experience":[12],"to":[13,33,37,57],"deal":[14],"with":[15],"complicated":[16],"management":[18],"system":[19],"and":[20,95],"wide":[21],"range":[22],"Key":[24],"Performance":[25],"Indicators":[26],"(KPIs).":[27],"There":[28],"are":[29],"many":[30],"indicators":[31,65],"related":[32],"each":[34,96],"other":[35],"due":[36],"definition":[39],"or":[40],"communication":[41],"process.":[42],"But":[43],"several":[44],"implicit":[45,61],"associations":[46],"still":[47],"exist":[48],"among":[49,64],"these":[50],"KPIs.":[51],"This":[52],"paper":[53],"proposes":[54],"an":[55],"approach":[56,88],"figure":[58],"out":[59],"linear":[62],"relationship":[63,102],"clearly":[66],"in":[67],"which":[68],"a":[69],"new":[70],"clustering":[71],"technique":[72],"is":[73],"used":[74],"for":[75],"distinguishing":[76],"different":[77],"relationships.":[78],"Data":[79],"analysis":[80],"using":[81],"real":[82],"data":[84,92],"shows":[85],"that":[86],"can":[89,98],"well":[90],"divide":[91],"into":[93],"clusters,":[94],"cluster":[97],"effectively":[99],"reflect":[100],"between":[103],"indicators.":[104]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}