{"id":"https://openalex.org/W2037354245","doi":"https://doi.org/10.1109/skg.2011.49","title":"Quality-Driven Hierarchical Clustering Algorithm for Service Intelligence Computation","display_name":"Quality-Driven Hierarchical Clustering Algorithm for Service Intelligence Computation","publication_year":2011,"publication_date":"2011-10-01","ids":{"openalex":"https://openalex.org/W2037354245","doi":"https://doi.org/10.1109/skg.2011.49","mag":"2037354245"},"language":"en","primary_location":{"id":"doi:10.1109/skg.2011.49","is_oa":false,"landing_page_url":"https://doi.org/10.1109/skg.2011.49","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 Seventh International Conference on Semantics, Knowledge and Grids","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/A5102875268","display_name":"Zhao Yun","orcid":"https://orcid.org/0000-0002-4432-9305"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yun Wei Zhao","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China","School of Software, Tsinghua University Beijing China"],"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"School of Software, Tsinghua University Beijing China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058644115","display_name":"Chi\u2010Hung Chi","orcid":"https://orcid.org/0000-0001-7271-1926"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chi-Hung Chi","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China","School of Software, Tsinghua University Beijing China"],"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"School of Software, Tsinghua University Beijing China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105841630","display_name":"Chen Ding","orcid":"https://orcid.org/0000-0003-0013-3439"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chen DIng","raw_affiliation_strings":["Department of Computer Science, Ryerson University, Toronto, Canada","Department of computer science, Ryerson University, Toronto, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Ryerson University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]},{"raw_affiliation_string":"Department of computer science, Ryerson University, Toronto, ON, Canada","institution_ids":["https://openalex.org/I530967"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102875268"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09225895,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":null,"first_page":"107","last_page":"113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9983000159263611,"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":0.9983000159263611,"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":0.9966999888420105,"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/T11106","display_name":"Data Management and Algorithms","score":0.9894000291824341,"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":0.8071715235710144},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7673166394233704},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.6774024963378906},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6386977434158325},{"id":"https://openalex.org/keywords/homogeneity","display_name":"Homogeneity (statistics)","score":0.567898690700531},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.5534034371376038},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.5225990414619446},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5105017423629761},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.48676085472106934},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4654940068721771},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.4238213002681732},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.41659337282180786},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.41416794061660767},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30733591318130493}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8071715235710144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7673166394233704},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.6774024963378906},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6386977434158325},{"id":"https://openalex.org/C142259097","wikidata":"https://www.wikidata.org/wiki/Q5891314","display_name":"Homogeneity (statistics)","level":2,"score":0.567898690700531},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.5534034371376038},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.5225990414619446},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5105017423629761},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.48676085472106934},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4654940068721771},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.4238213002681732},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.41659337282180786},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.41416794061660767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30733591318130493},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/skg.2011.49","is_oa":false,"landing_page_url":"https://doi.org/10.1109/skg.2011.49","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 Seventh International Conference on Semantics, Knowledge and Grids","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":17,"referenced_works":["https://openalex.org/W1499011913","https://openalex.org/W1503653277","https://openalex.org/W1561994953","https://openalex.org/W1577289782","https://openalex.org/W1594924988","https://openalex.org/W1952993614","https://openalex.org/W2001860992","https://openalex.org/W2004925710","https://openalex.org/W2020701082","https://openalex.org/W2028293067","https://openalex.org/W2050932686","https://openalex.org/W2057828472","https://openalex.org/W2127158403","https://openalex.org/W2186602557","https://openalex.org/W6630226436","https://openalex.org/W6633521410","https://openalex.org/W6634422543"],"related_works":["https://openalex.org/W2160785859","https://openalex.org/W2390610678","https://openalex.org/W3140018618","https://openalex.org/W3146523624","https://openalex.org/W2188840951","https://openalex.org/W2241871771","https://openalex.org/W2897883632","https://openalex.org/W2171583777","https://openalex.org/W2555816786","https://openalex.org/W2407786351"],"abstract_inverted_index":{"Clustering":[0],"is":[1,39],"an":[2],"important":[3],"technique":[4],"for":[5,96],"intelligence":[6],"computation":[7],"such":[8],"as":[9,100],"trust,":[10],"recommendation,":[11],"reputation,":[12],"and":[13,23,73,83,93,128,140],"requirement":[14],"elicitation.":[15],"With":[16],"the":[17,24,31,34,48,52,69,115,133,136,145],"user":[18,44],"centric":[19],"nature":[20],"of":[21,27,33,58],"service":[22],"user's":[25],"lack":[26],"prior":[28],"knowledge":[29],"on":[30,40,47,68,121],"distribution":[32,127],"raw":[35],"data,":[36],"one":[37],"challenge":[38],"how":[41],"to":[42,60,113],"associate":[43],"quality":[45,138],"requirements":[46],"clustering":[49,71,78,87],"results":[50],"with":[51,89,124],"algorithmic":[53],"output":[54,99],"properties":[55],"(e.g.":[56],"number":[57],"clusters":[59],"be":[61],"targeted).":[62],"In":[63],"this":[64],"paper,":[65],"we":[66,105],"focus":[67],"hierarchical":[70,77],"process":[72],"propose":[74],"two":[75],"quality-driven":[76],"algorithms,":[79,88],"HBH":[80],"(homogeneity-based":[81],"hierarchical)":[82,86],"HDH":[84,134],"(homogeneity-driven":[85],"minimum":[90],"acceptable":[91],"homogeneity":[92],"relative":[94],"population":[95],"each":[97],"cluster":[98],"their":[101],"input":[102],"criteria.":[103],"Furthermore,":[104],"also":[106],"give":[107],"a":[108],"HDH-approximation":[109,141],"algorithm":[110],"in":[111],"order":[112],"address":[114],"time":[116],"performance":[117],"issue.":[118],"Experimental":[119],"study":[120],"data":[122],"sets":[123],"different":[125],"density":[126],"dispersion":[129],"levels":[130],"shows":[131],"that":[132],"gives":[135],"best":[137],"result":[139],"can":[142],"significantly":[143],"improve":[144],"execution":[146],"time.":[147]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}