{"id":"https://openalex.org/W4250967542","doi":"https://doi.org/10.4018/ijrsda.2015010105","title":"Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System","display_name":"Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W4250967542","doi":"https://doi.org/10.4018/ijrsda.2015010105"},"language":"en","primary_location":{"id":"doi:10.4018/ijrsda.2015010105","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijrsda.2015010105","pdf_url":null,"source":{"id":"https://openalex.org/S4210215979","display_name":"International Journal of Rough Sets and Data Analysis","issn_l":"2334-4598","issn":["2334-4598","2334-4601"],"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 Rough Sets and Data Analysis","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/A5068145355","display_name":"Hannah Inbarani H.","orcid":null},"institutions":[{"id":"https://openalex.org/I141431873","display_name":"Periyar University","ror":"https://ror.org/05crs8s98","country_code":"IN","type":"education","lineage":["https://openalex.org/I141431873"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Hannah Inbarani H","raw_affiliation_strings":["Department of Computer Science, Periyar University, Salem, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Periyar University, Salem, India","institution_ids":["https://openalex.org/I141431873"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021637696","display_name":"Selva Kumar S","orcid":null},"institutions":[{"id":"https://openalex.org/I141431873","display_name":"Periyar University","ror":"https://ror.org/05crs8s98","country_code":"IN","type":"education","lineage":["https://openalex.org/I141431873"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Selva Kumar S","raw_affiliation_strings":["Department of Computer Science, Periyar University, Salem, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Periyar University, Salem, India","institution_ids":["https://openalex.org/I141431873"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068145355"],"corresponding_institution_ids":["https://openalex.org/I141431873"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.33860988,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"2","issue":"1","first_page":"70","last_page":"87"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.9882000088691711,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8731001615524292},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6854314804077148},{"id":"https://openalex.org/keywords/bookmarking","display_name":"Bookmarking","score":0.6109656691551208},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.5516397953033447},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5361473560333252},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.535474419593811},{"id":"https://openalex.org/keywords/firefly-algorithm","display_name":"Firefly algorithm","score":0.5158204436302185},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5111241936683655},{"id":"https://openalex.org/keywords/document-clustering","display_name":"Document clustering","score":0.4991121292114258},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.43489766120910645},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.32667890191078186},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2899717688560486},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12037813663482666}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8731001615524292},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6854314804077148},{"id":"https://openalex.org/C176504155","wikidata":"https://www.wikidata.org/wiki/Q4943254","display_name":"Bookmarking","level":2,"score":0.6109656691551208},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.5516397953033447},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5361473560333252},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.535474419593811},{"id":"https://openalex.org/C154982244","wikidata":"https://www.wikidata.org/wiki/Q5451844","display_name":"Firefly algorithm","level":3,"score":0.5158204436302185},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5111241936683655},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.4991121292114258},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.43489766120910645},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32667890191078186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2899717688560486},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12037813663482666},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4018/ijrsda.2015010105","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijrsda.2015010105","pdf_url":null,"source":{"id":"https://openalex.org/S4210215979","display_name":"International Journal of Rough Sets and Data Analysis","issn_l":"2334-4598","issn":["2334-4598","2334-4601"],"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 Rough Sets and Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W95298688","https://openalex.org/W1030501565","https://openalex.org/W1523741643","https://openalex.org/W1576130111","https://openalex.org/W1756335358","https://openalex.org/W1964066675","https://openalex.org/W1967165005","https://openalex.org/W1984779822","https://openalex.org/W1988769343","https://openalex.org/W1992419399","https://openalex.org/W1998778961","https://openalex.org/W2010780385","https://openalex.org/W2023943573","https://openalex.org/W2040187729","https://openalex.org/W2041459465","https://openalex.org/W2050720134","https://openalex.org/W2056168656","https://openalex.org/W2064173066","https://openalex.org/W2085166945","https://openalex.org/W2094521916","https://openalex.org/W2125301736","https://openalex.org/W2148002238","https://openalex.org/W2154943049","https://openalex.org/W2165448986","https://openalex.org/W3151070811"],"related_works":["https://openalex.org/W4252751414","https://openalex.org/W1508683831","https://openalex.org/W2112242382","https://openalex.org/W2066393948","https://openalex.org/W1964957699","https://openalex.org/W2183555234","https://openalex.org/W4250494040","https://openalex.org/W4309047791","https://openalex.org/W2789578134","https://openalex.org/W1589938575"],"abstract_inverted_index":{"Social":[0,70],"tagging":[1],"is":[2,15,40,53,101],"one":[3],"of":[4,8,12,19,43,69,123],"the":[5,34,41,45,60,67,76,98,104,121,124],"vital":[6],"attributes":[7],"WEB2.0.":[9],"The":[10,50,117],"challenge":[11],"Web":[13],"2.0":[14,36],"a":[16,23,79],"gigantic":[17],"measure":[18],"information":[20],"created":[21],"over":[22],"brief":[24],"time.":[25],"Tags":[26],"are":[27],"broadly":[28],"used":[29],"to":[30],"interpret":[31],"and":[32,58,109],"arrange":[33],"web":[35],"assets.":[37],"Tag":[38],"clustering":[39,52,87,90,108],"procedure":[42],"grouping":[44],"comparable":[46],"tags":[47,91],"into":[48],"clusters.":[49],"tag":[51],"extremely":[54],"valuable":[55],"for":[56,66,89],"researching":[57],"organizing":[59],"web2.":[61],"0":[62],"resources":[63],"furthermore":[64],"critical":[65],"achievement":[68],"Bookmarking":[71],"frameworks.":[72],"In":[73],"this":[74],"paper,":[75],"authors":[77],"proposed":[78,99],"hybrid":[80],"Tolerance":[81],"Rough":[82],"Set":[83],"Based":[84],"Firefly":[85],"(TRS-Firefly-K-Means)":[86],"algorithm":[88,106],"in":[92],"social":[93],"systems.":[94],"At":[95],"that":[96],"stage,":[97],"system":[100],"contrasted":[102],"with":[103],"benchmark":[105],"K-Means":[107],"Particle":[110],"Swarm":[111],"optimization":[112],"(PSO)":[113],"based":[114],"Clustering":[115],"technique.":[116],"experimental":[118],"analysis":[119],"outlines":[120],"viability":[122],"suggested":[125],"methodology.":[126]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}
