{"id":"https://openalex.org/W2151100221","doi":"https://doi.org/10.1109/icsmc.2008.4811878","title":"Semantic feature reduction in chinese document clustering","display_name":"Semantic feature reduction in chinese document clustering","publication_year":2008,"publication_date":"2008-10-01","ids":{"openalex":"https://openalex.org/W2151100221","doi":"https://doi.org/10.1109/icsmc.2008.4811878","mag":"2151100221"},"language":"en","primary_location":{"id":"doi:10.1109/icsmc.2008.4811878","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2008.4811878","pdf_url":null,"source":{"id":"https://openalex.org/S4210195764","display_name":"Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics","issn_l":"1062-922X","issn":["1062-922X","2577-1655"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Systems, Man and Cybernetics","raw_type":"proceedings-article"},"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/A5102155605","display_name":"Xianjun Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianjun Meng","raw_affiliation_strings":["ICRC Laboratory, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ICRC Laboratory, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101895423","display_name":"Qingcai Chen","orcid":"https://orcid.org/0000-0001-8473-7293"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingcai Chen","raw_affiliation_strings":["ICRC Laboratory, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ICRC Laboratory, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063551745","display_name":"Xiaolong Wang","orcid":"https://orcid.org/0000-0001-7366-6434"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolong Wang","raw_affiliation_strings":["ICRC Laboratory, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ICRC Laboratory, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"3721","last_page":"3726"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9958000183105469,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9958000183105469,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.994700014591217,"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"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9911999702453613,"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":0.8449205160140991},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7368118762969971},{"id":"https://openalex.org/keywords/document-clustering","display_name":"Document clustering","score":0.5964453220367432},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5778255462646484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5643819570541382},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5345918536186218},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5308117866516113},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.49084025621414185},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.48529142141342163},{"id":"https://openalex.org/keywords/tf\u2013idf","display_name":"tf\u2013idf","score":0.48394444584846497},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4719982147216797},{"id":"https://openalex.org/keywords/vector-space-model","display_name":"Vector space model","score":0.45380595326423645},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.4513314664363861},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.4378061294555664},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.43624767661094666},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42278528213500977},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.10893550515174866}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8449205160140991},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7368118762969971},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.5964453220367432},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5778255462646484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5643819570541382},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5345918536186218},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5308117866516113},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.49084025621414185},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.48529142141342163},{"id":"https://openalex.org/C81758059","wikidata":"https://www.wikidata.org/wiki/Q796584","display_name":"tf\u2013idf","level":3,"score":0.48394444584846497},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4719982147216797},{"id":"https://openalex.org/C89686163","wikidata":"https://www.wikidata.org/wiki/Q1187982","display_name":"Vector space model","level":2,"score":0.45380595326423645},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.4513314664363861},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.4378061294555664},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.43624767661094666},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42278528213500977},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.10893550515174866},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsmc.2008.4811878","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2008.4811878","pdf_url":null,"source":{"id":"https://openalex.org/S4210195764","display_name":"Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics","issn_l":"1062-922X","issn":["1062-922X","2577-1655"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Systems, Man and Cybernetics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5799999833106995,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W94178392","https://openalex.org/W130742730","https://openalex.org/W287031571","https://openalex.org/W1516726196","https://openalex.org/W1521439890","https://openalex.org/W1651093245","https://openalex.org/W1736751365","https://openalex.org/W1854214752","https://openalex.org/W1982024311","https://openalex.org/W2009106802","https://openalex.org/W2074449313","https://openalex.org/W2100958137","https://openalex.org/W2112210867","https://openalex.org/W2116237179","https://openalex.org/W2121672615","https://openalex.org/W2121996546","https://openalex.org/W2133517430","https://openalex.org/W2137763598","https://openalex.org/W2138621811","https://openalex.org/W2146811452","https://openalex.org/W2147152072","https://openalex.org/W2165612380","https://openalex.org/W2319660501","https://openalex.org/W2795859997","https://openalex.org/W2999729612","https://openalex.org/W4244268470","https://openalex.org/W4251146235","https://openalex.org/W6603866199","https://openalex.org/W6610416351","https://openalex.org/W6631328736","https://openalex.org/W6637231022","https://openalex.org/W6637914374","https://openalex.org/W6639055396","https://openalex.org/W7019129576"],"related_works":["https://openalex.org/W2549551998","https://openalex.org/W3036265557","https://openalex.org/W2900987978","https://openalex.org/W2975262101","https://openalex.org/W2163957714","https://openalex.org/W3109760095","https://openalex.org/W3150575538","https://openalex.org/W3028990185","https://openalex.org/W4240717064","https://openalex.org/W2035503345"],"abstract_inverted_index":{"Text":[0],"clustering":[1,36,44,86,103,115,200,240],"techniques":[2],"were":[3],"usually":[4],"used":[5,153],"to":[6,19],"structure":[7],"the":[8,64,76,85,98,114,173,197,231,245],"text":[9,35,68,156,221],"documents":[10,177],"into":[11],"topic":[12],"related":[13],"groups":[14],"which":[15,38,152,215],"can":[16,133,171,229],"facilitate":[17],"users":[18],"get":[20],"a":[21,81,145,162,238],"comprehensive":[22],"understanding":[23],"on":[24,47,84,161],"corpus":[25],"or":[26,193],"results":[27,224],"from":[28,40,111,135],"information":[29],"retrieval":[30],"system.":[31],"Most":[32],"of":[33,63,78,102,176,199,244],"existing":[34],"algorithm":[37,104,214],"derived":[39],"traditional":[41,182],"formatted":[42],"data":[43],"heavily":[45],"rely":[46],"term":[48],"analysis":[49,137,147],"methods":[50],"and":[51,100,108,167,170,187,235],"adopted":[52],"vector":[53,74],"space":[54,175,233],"model":[55],"(VSM)":[56],"as":[57,70,118],"their":[58],"document":[59],"representation.":[60],"But":[61],"because":[62],"essential":[65],"characteristic":[66],"underlying":[67],"such":[69],"high":[71],"dimensionality":[72],"features":[73],"space,":[75],"problem":[77],"sparseness":[79],"has":[80],"strong":[82],"impact":[83],"algorithm.":[87,201],"So":[88],"feature":[89,149,174,183,208,232],"reduction":[90,150,184,209],"is":[91,116,127],"an":[92,119],"important":[93],"preprocess":[94],"step":[95],"for":[96],"improving":[97],"efficiency":[99],"accuracy":[101,198,241],"by":[105],"removing":[106],"redundant":[107],"irrelevant":[109],"terms":[110,243],"corpus.":[112],"Even":[113],"considered":[117],"unsupervised":[120],"learning":[121],"method,":[122],"but":[123],"in":[124,154,220,242],"text,":[125],"there":[126],"still":[128],"some":[129],"priori":[130],"knowledge":[131],"we":[132,143,205],"use":[134],"NLP":[136],"based":[138,148],"approach.":[139],"In":[140,202],"this":[141],"paper,":[142],"propose":[144],"semantic":[146],"method":[151,159,185,210,228],"Chinese":[155],"clustering.":[157,222],"Our":[158],"bases":[160],"dedicated":[163],"Part-of-Speech":[164],"tags":[165],"selection":[166],"synonyms":[168],"consolidation":[169],"reduce":[172,230],"more":[178],"effectively":[179],"compared":[180],"with":[181],"tfidf":[186],"stopwords":[188],"removal;":[189],"meanwhile":[190,236],"it":[191],"preserves":[192],"sometimes":[194],"even":[195],"improves":[196],"our":[203,207,227],"experiment,":[204],"tested":[206],"using":[211],"bisecting":[212],"k-means":[213],"was":[216],"proved":[217],"be":[218],"efficient":[219],"The":[223],"show":[225],"that":[226],"significantly,":[234],"have":[237],"better":[239],"purity.":[246]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}