{"id":"https://openalex.org/W2139095903","doi":"https://doi.org/10.1109/icmlc.2005.1527306","title":"TCBLHT: a new method of hierarchical text clustering","display_name":"TCBLHT: a new method of hierarchical text clustering","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W2139095903","doi":"https://doi.org/10.1109/icmlc.2005.1527306","mag":"2139095903"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2005.1527306","doi":"https://doi.org/10.1109/icmlc.2005.1527306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2005.1527306","pdf_url":null,"source":{"id":"https://openalex.org/S4306419646","display_name":"International Conference on Machine Learning and Cybernetics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2005 International Conference on Machine Learning and Cybernetics","raw_type":"proceedings-article"},"volume":null,"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/A5100880439","display_name":"Jian-Suo Xu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I75955062","display_name":"Henan Normal University","ror":"https://ror.org/00s13br28","country_code":"CN","type":"education","lineage":["https://openalex.org/I75955062"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian-Suo Xu","raw_affiliation_strings":["School of Economy and Management, Henan Normal University, Xinxiang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economy and Management, Henan Normal University, Xinxiang, China","institution_ids":["https://openalex.org/I75955062"]}]},{"author_position":"last","author":{"id":null,"display_name":"Li Wang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li Wang","raw_affiliation_strings":["School of Computer, Anshan University of Science and Technology, Anshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer, Anshan University of Science and Technology, Anshan, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.477,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63924873,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":null,"first_page":"2178","last_page":"2181 Vol. 4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9496999979019165,"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":0.9496999979019165,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.03700000047683716,"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/T10028","display_name":"Topic Modeling","score":0.0066999997943639755,"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/latent-semantic-analysis","display_name":"latent semantic analysis","score":0.9789999723434448},{"id":"https://openalex.org/keywords/term-weighting","display_name":"term weighting","score":0.6700000166893005},{"id":"https://openalex.org/keywords/vector-space-model","display_name":"vector space model","score":0.9380000233650208},{"id":"https://openalex.org/keywords/semantic-relations","display_name":"semantic relations","score":0.8679999709129333},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.6710000038146973},{"id":"https://openalex.org/keywords/document-clustering","display_name":"document clustering","score":0.47999998927116394}],"concepts":[{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.770391583442688},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7348649501800537},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6880395412445068},{"id":"https://openalex.org/C167984511","wikidata":"https://www.wikidata.org/wiki/Q17003931","display_name":"Brown clustering","level":5,"score":0.6459857821464539},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.6026740074157715},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.5748488903045654},{"id":"https://openalex.org/C82261393","wikidata":"https://www.wikidata.org/wiki/Q17038699","display_name":"Hierarchical clustering of networks","level":5,"score":0.5681528449058533},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.5227063894271851},{"id":"https://openalex.org/C89686163","wikidata":"https://www.wikidata.org/wiki/Q1187982","display_name":"Vector space model","level":2,"score":0.5190956592559814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49085503816604614},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.48581814765930176},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.473862886428833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4587147533893585},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.4561459720134735},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3633137345314026}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc.2005.1527306","doi":"https://doi.org/10.1109/icmlc.2005.1527306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2005.1527306","pdf_url":null,"source":{"id":"https://openalex.org/S4306419646","display_name":"International Conference on Machine Learning and Cybernetics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2005 International Conference on Machine Learning and Cybernetics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"display_name":"Quality education","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/4"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2091273188","https://openalex.org/W2093432797","https://openalex.org/W2147152072","https://openalex.org/W2351692326","https://openalex.org/W4245049592","https://openalex.org/W4285719527","https://openalex.org/W6705582161"],"related_works":["https://openalex.org/W2488882714","https://openalex.org/W4292477917","https://openalex.org/W1574580469","https://openalex.org/W2534504385","https://openalex.org/W2592952084","https://openalex.org/W2140131129","https://openalex.org/W2060765209","https://openalex.org/W205292606","https://openalex.org/W4241340851","https://openalex.org/W2130194910"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3],"new":[4],"method":[5,49,90],"of":[6,13,62,69,94,102],"hierarchical":[7,19,35,39,53],"text":[8,27,45,54,103],"clustering":[9,28],"based":[10],"on":[11],"combination":[12],"latent":[14],"semantic":[15,72],"analysis":[16,83],"(LSA)":[17],"and":[18,56,84,96,100],"TGSOM,":[20],"which":[21],"is":[22,41,74],"called":[23],"TCBLHT":[24,48,89],"method.":[25],"The":[26,47],"result":[29],"using":[30,66],"traditional":[31],"methods":[32],"cannot":[33],"show":[34],"structure,":[36],"however,":[37],"the":[38,67,77,92,98],"structure":[40],"very":[42],"important":[43],"in":[44,76],"clustering.":[46,104],"can":[50],"automatically":[51],"achieve":[52],"clustering,":[55],"establishes":[57],"vector":[58,78],"space":[59,79],"model":[60],"(VSM)":[61],"term":[63],"weight":[64],"by":[65],"theory":[68,82],"LSA,":[70],"then":[71],"relation":[73],"included":[75],"model.":[80],"Both":[81],"experimental":[85],"results":[86],"confirm":[87],"that":[88],"decreases":[91],"number":[93],"vector,":[95],"enhances":[97],"efficiency":[99],"precision":[101]},"counts_by_year":[],"updated_date":"2026-10-11T09:32:39.932853","created_date":"2025-10-10T00:00:00"}
