{"id":"https://openalex.org/W2121853761","doi":"https://doi.org/10.1109/icdm.2002.1183895","title":"Iterative clustering of high dimensional text data augmented by local search","display_name":"Iterative clustering of high dimensional text data augmented by local search","publication_year":2003,"publication_date":"2003-06-26","ids":{"openalex":"https://openalex.org/W2121853761","doi":"https://doi.org/10.1109/icdm.2002.1183895","mag":"2121853761"},"language":"en","primary_location":{"id":"doi:10.1109/icdm.2002.1183895","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2002.1183895","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","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/A5063459703","display_name":"Inderjit S. Dhillon","orcid":"https://orcid.org/0000-0002-2759-1416"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"I.S. Dhillon","raw_affiliation_strings":["Department of Computer Sciences, University of Technology, Austin, TX, USA","Dept. of Comput. Sci., Texas Univ., Austin, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Sciences, University of Technology, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]},{"raw_affiliation_string":"Dept. of Comput. Sci., Texas Univ., Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004682787","display_name":"Yuqiang Guan","orcid":null},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuqiang Guan","raw_affiliation_strings":["Department of Computer Sciences, University of Technology, Austin, TX, USA","Dept. of Comput. Sci., Texas Univ., Austin, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Sciences, University of Technology, Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]},{"raw_affiliation_string":"Dept. of Comput. Sci., Texas Univ., Austin, TX, USA","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111938420","display_name":"J. Kogan","orcid":null},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]},{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Kogan","raw_affiliation_strings":["Department of Mathematics and Statistics, University of Maryland, Baltimore, MD, USA","\u00a0 University of Maryland, Baltimore County"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of Maryland, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"\u00a0 University of Maryland, Baltimore County","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.2291,"has_fulltext":false,"cited_by_count":128,"citation_normalized_percentile":{"value":0.97379336,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"131","last_page":"138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9984999895095825,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9927999973297119,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8776757121086121},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.6403905749320984},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6373090147972107},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5316771864891052},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.5245217084884644},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.5015857219696045},{"id":"https://openalex.org/keywords/trigonometric-functions","display_name":"Trigonometric functions","score":0.49648553133010864},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.49571001529693604},{"id":"https://openalex.org/keywords/affinity-propagation","display_name":"Affinity propagation","score":0.48770031332969666},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4647841453552246},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.45870885252952576},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.4258579611778259},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4225614070892334},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3649081587791443},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29093050956726074},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2706393003463745},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08200976252555847},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07719847559928894}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8776757121086121},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.6403905749320984},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6373090147972107},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5316771864891052},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.5245217084884644},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.5015857219696045},{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.49648553133010864},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.49571001529693604},{"id":"https://openalex.org/C109659709","wikidata":"https://www.wikidata.org/wiki/Q3407504","display_name":"Affinity propagation","level":5,"score":0.48770031332969666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4647841453552246},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.45870885252952576},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.4258579611778259},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4225614070892334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3649081587791443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29093050956726074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2706393003463745},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08200976252555847},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07719847559928894},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icdm.2002.1183895","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2002.1183895","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.140.3309","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.3309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.utexas.edu/users/inderjit/public_papers/iterative_icdm02.ps","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.81.8317","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.8317","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.utexas.edu/users/inderjit/public_papers/iterative_icdm02.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W147860157","https://openalex.org/W1484739396","https://openalex.org/W1489608363","https://openalex.org/W1490760466","https://openalex.org/W1493454437","https://openalex.org/W1493526108","https://openalex.org/W1498782169","https://openalex.org/W1522930108","https://openalex.org/W1533169541","https://openalex.org/W1574845294","https://openalex.org/W1575476631","https://openalex.org/W1634005169","https://openalex.org/W1790954942","https://openalex.org/W2127218421","https://openalex.org/W2161455936","https://openalex.org/W2799061466","https://openalex.org/W4205687621","https://openalex.org/W4239216443","https://openalex.org/W4254197176","https://openalex.org/W6606145560","https://openalex.org/W6614676750","https://openalex.org/W6630018080","https://openalex.org/W6631307971","https://openalex.org/W6634394891","https://openalex.org/W6678914141"],"related_works":["https://openalex.org/W2245611357","https://openalex.org/W2358586643","https://openalex.org/W1975195692","https://openalex.org/W1528194447","https://openalex.org/W2794209582","https://openalex.org/W2567087402","https://openalex.org/W2897883632","https://openalex.org/W2065347048","https://openalex.org/W2362911195","https://openalex.org/W2350312206"],"abstract_inverted_index":{"The":[0],"k-means":[1,11,23,117,127],"algorithm":[2,146],"with":[3,115],"cosine":[4],"similarity,":[5],"also":[6],"known":[7],"as":[8],"the":[9,54,112,140],"spherical":[10,22,116],"algorithm,":[12],"is":[13,130],"a":[14,48,62,72,84,95,101,107,119],"popular":[15],"method":[16],"for":[17],"clustering":[18,74,128,148],"document":[19],"collections.":[20],"However":[21],"can":[24],"often":[25,124],"yield":[26],"qualitatively":[27,125],"poor":[28],"results,":[29],"especially":[30],"when":[31],"cluster":[32],"sizes":[33],"are":[34],"small,":[35],"say":[36],"25-30":[37],"documents":[38],"per":[39],"cluster,":[40],"where":[41],"it":[42],"tends":[43],"to":[44,106,138],"get":[45],"stuck":[46],"at":[47],"local":[49,63,109],"maximum":[50],"far":[51],"away":[52],"from":[53],"optimal":[55],"solution.":[56],"In":[57],"this":[58],"paper,":[59],"we":[60,67],"present":[61,134],"search":[64],"procedure,":[65],"which":[66],"call":[68],"'first-variation\"":[69],"that":[70,123],"refines":[71],"given":[73],"by":[75,143],"incrementally":[76],"moving":[77],"data":[78],"points":[79],"between":[80],"clusters,":[81],"thus":[82],"achieving":[83],"higher":[85],"objective":[86],"function":[87],"value.":[88],"An":[89],"enhancement":[90],"of":[91,97],"first":[92],"variation":[93],"allows":[94],"chain":[96],"such":[98],"moves":[99],"in":[100,147],"Kernighan-Lin":[102],"fashion":[103],"and":[104,129,150],"leads":[105],"better":[108],"maximum.":[110],"Combining":[111],"enhanced":[113],"first-variation":[114],"yields":[118],"powerful":[120],"\"ping-pong\"":[121],"strategy":[122],"improves":[126],"computationally":[131],"efficient.":[132],"We":[133],"several":[135],"experimental":[136],"results":[137],"highlight":[139],"improvement":[141],"achieved":[142],"our":[144],"proposed":[145],"high-dimensional":[149],"sparse":[151],"text":[152],"data.":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":12}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
