{"id":"https://openalex.org/W2964354541","doi":"https://doi.org/10.4018/ijitwe.2019100104","title":"An Enhanced and Efficient Multi-View Clustering Trust Inference Approach by GA Model","display_name":"An Enhanced and Efficient Multi-View Clustering Trust Inference Approach by GA Model","publication_year":2019,"publication_date":"2019-08-07","ids":{"openalex":"https://openalex.org/W2964354541","doi":"https://doi.org/10.4018/ijitwe.2019100104","mag":"2964354541"},"language":"en","primary_location":{"id":"doi:10.4018/ijitwe.2019100104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijitwe.2019100104","pdf_url":null,"source":{"id":"https://openalex.org/S152329874","display_name":"International Journal of Information Technology and Web Engineering","issn_l":"1554-1045","issn":["1554-1045","1554-1053"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Information Technology and Web Engineering","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":"https://openalex.org/A5111961112","display_name":"M. Ravichandran","orcid":null},"institutions":[{"id":"https://openalex.org/I209674108","display_name":"Shadan Hospital and Institute of Medical Sciences","ror":"https://ror.org/02cnc0j97","country_code":"IN","type":"education","lineage":["https://openalex.org/I209674108"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ravichandran M","raw_affiliation_strings":["Shadan College of Engineering and Technolgoy, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shadan College of Engineering and Technolgoy, Hyderabad, India","institution_ids":["https://openalex.org/I209674108"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113299091","display_name":"K. Subramanian","orcid":null},"institutions":[{"id":"https://openalex.org/I209674108","display_name":"Shadan Hospital and Institute of Medical Sciences","ror":"https://ror.org/02cnc0j97","country_code":"IN","type":"education","lineage":["https://openalex.org/I209674108"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Subramanian K M","raw_affiliation_strings":["Shadan College of Engineering and Technolgoy, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shadan College of Engineering and Technolgoy, Hyderabad, India","institution_ids":["https://openalex.org/I209674108"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073571804","display_name":"R. Jothikumar","orcid":null},"institutions":[{"id":"https://openalex.org/I209674108","display_name":"Shadan Hospital and Institute of Medical Sciences","ror":"https://ror.org/02cnc0j97","country_code":"IN","type":"education","lineage":["https://openalex.org/I209674108"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jothikumar R","raw_affiliation_strings":["Shadan College of Engineering and Technology, Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shadan College of Engineering and Technology, Hyderabad, India","institution_ids":["https://openalex.org/I209674108"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I209674108"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07886216,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":"4","first_page":"64","last_page":"78"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9962000250816345,"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":0.9962000250816345,"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/T10057","display_name":"Face and Expression Recognition","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9771000146865845,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7818015813827515},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7531894445419312},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7117242813110352},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6530173420906067},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5143888592720032},{"id":"https://openalex.org/keywords/affinity-propagation","display_name":"Affinity propagation","score":0.5082151889801025},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.4972377121448517},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4790262281894684},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4741104245185852},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.46130067110061646},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.43696972727775574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4003172814846039},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.385884165763855},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.3788423538208008},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2994590103626251},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.222694993019104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16725704073905945},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11472457647323608},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.10858291387557983},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06727573275566101}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7818015813827515},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7531894445419312},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7117242813110352},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6530173420906067},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5143888592720032},{"id":"https://openalex.org/C109659709","wikidata":"https://www.wikidata.org/wiki/Q3407504","display_name":"Affinity propagation","level":5,"score":0.5082151889801025},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.4972377121448517},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4790262281894684},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4741104245185852},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.46130067110061646},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.43696972727775574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4003172814846039},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.385884165763855},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.3788423538208008},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2994590103626251},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.222694993019104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16725704073905945},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11472457647323608},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.10858291387557983},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06727573275566101},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/ijitwe.2019100104","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijitwe.2019100104","pdf_url":null,"source":{"id":"https://openalex.org/S152329874","display_name":"International Journal of Information Technology and Web Engineering","issn_l":"1554-1045","issn":["1554-1045","1554-1053"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Information Technology and Web Engineering","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:jitwe0:v:14:y:2019:i:4:p:64-78","is_oa":false,"landing_page_url":"https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJITWE.2019100104","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.46000000834465027,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1908497220","https://openalex.org/W1971732258","https://openalex.org/W1992905164","https://openalex.org/W2032642422","https://openalex.org/W2035748848","https://openalex.org/W2043819533","https://openalex.org/W2046218887","https://openalex.org/W2049217466","https://openalex.org/W2054042238","https://openalex.org/W2076086957","https://openalex.org/W2085265340","https://openalex.org/W2101754857","https://openalex.org/W2105709960","https://openalex.org/W2117346966","https://openalex.org/W2121207090","https://openalex.org/W2125151038","https://openalex.org/W2184840276","https://openalex.org/W2210977594","https://openalex.org/W2214993060","https://openalex.org/W2239498736","https://openalex.org/W2301166038","https://openalex.org/W2471265345","https://openalex.org/W2516780539","https://openalex.org/W2526561371","https://openalex.org/W2767919445","https://openalex.org/W2808465901","https://openalex.org/W2964070360"],"related_works":["https://openalex.org/W357196361","https://openalex.org/W3136298662","https://openalex.org/W2027314909","https://openalex.org/W1036938216","https://openalex.org/W3109425891","https://openalex.org/W2113714434","https://openalex.org/W3096637473","https://openalex.org/W2377792686","https://openalex.org/W4200439127","https://openalex.org/W829658220"],"abstract_inverted_index":{"Multi-view":[0],"affinity":[1,63,106],"propagation":[2],"(MAP)":[3],"methods":[4],"are":[5],"widely":[6],"accepted":[7],"techniques,":[8],"measure":[9],"the":[10,37,81,88,100,104,121,130,140],"within-view":[11],"clustering":[12,14,59,142],"and":[13,20,26,46,70,144],"consistency.":[15],"These":[16],"suffer":[17,42],"from":[18,43],"similarity":[19,27,69,101],"correlation":[21],"between":[22,74],"clusters.":[23],"The":[24],"trust":[25,91],"measured":[28],"was":[29],"introduced":[30],"as":[31],"a":[32,55],"new":[33],"approach":[34],"to":[35,49,128],"overcome":[36],"problem.":[38],"But":[39],"these":[40],"approaches":[41],"low":[44],"accuracy":[45,143],"coverage":[47],"due":[48],"avoidance":[50],"of":[51,99,110],"implicit":[52,71],"trust.":[53,72],"So,":[54],"framework":[56],"called":[57],"multi-view":[58,141],"based":[60,102,115],"on":[61,103,116,120],"gray":[62,105],"(MVC-GA)":[64],"created":[65],"by":[66,79],"integrating":[67],"both":[68],"Similarity":[73],"two":[75],"clusters":[76],"is":[77,113,134],"obtained":[78],"applying":[80],"Pearson":[82],"Correlation":[83],"Coefficient-based":[84],"similarity.":[85],"It":[86,133],"utilizes":[87],"collaborative":[89],"filter-based":[90],"evaluation":[92],"for":[93],"each":[94],"clustered":[95],"view":[96],"in":[97],"terms":[98],"nn":[107],"algorithm.":[108],"Classification":[109],"incomplete":[111],"occurrences":[112],"addressed":[114],"GA":[117],"Function.":[118],"Experiments":[119],"benchmark":[122],"data":[123],"sets":[124],"have":[125],"been":[126],"performed":[127],"validate":[129],"proposed":[131],"framework.":[132],"shown":[135],"that":[136],"MVC-GA":[137],"can":[138],"improve":[139],"coverage.":[145]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
