{"id":"https://openalex.org/W4415974268","doi":"https://doi.org/10.1016/j.procs.2025.09.168","title":"Asymptotic Properties of Kernel K-means and Its Comparison with Conventional K-means in High-Dimensional Settings","display_name":"Asymptotic Properties of Kernel K-means and Its Comparison with Conventional K-means in High-Dimensional Settings","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415974268","doi":"https://doi.org/10.1016/j.procs.2025.09.168"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2025.09.168","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.168","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2025.09.168","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026900749","display_name":"Kento Egashira","orcid":null},"institutions":[{"id":"https://openalex.org/I161296585","display_name":"Tokyo University of Science","ror":"https://ror.org/05sj3n476","country_code":"JP","type":"education","lineage":["https://openalex.org/I161296585"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kento Egashira","raw_affiliation_strings":["Department of Information Sciences, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 270-8510, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Sciences, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 270-8510, Japan","institution_ids":["https://openalex.org/I161296585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014708919","display_name":"Kazuyoshi Yata","orcid":"https://orcid.org/0000-0001-6814-4463"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuyoshi Yata","raw_affiliation_strings":["Institute of Mathematics, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Mathematics, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001988364","display_name":"Makoto Aoshima","orcid":"https://orcid.org/0000-0002-3791-7977"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Makoto Aoshima","raw_affiliation_strings":["Institute of Mathematics, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Mathematics, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8571, Japan","institution_ids":["https://openalex.org/I146399215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026900749"],"corresponding_institution_ids":["https://openalex.org/I161296585"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29029587,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"270","issue":null,"first_page":"495","last_page":"504"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.7160000205039978,"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":0.7160000205039978,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.08190000057220459,"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":0.07569999992847443,"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/kernel","display_name":"Kernel (algebra)","score":0.7692999839782715},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.7171000242233276},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5960999727249146},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.5943999886512756},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.5200999975204468},{"id":"https://openalex.org/keywords/polynomial","display_name":"Polynomial","score":0.5131999850273132},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4603999853134155}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7692999839782715},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.7171000242233276},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5960999727249146},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.5943999886512756},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5629000067710876},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.5200999975204468},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.5131999850273132},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4603999853134155},{"id":"https://openalex.org/C27406209","wikidata":"https://www.wikidata.org/wiki/Q6394203","display_name":"Kernel smoother","level":5,"score":0.42289999127388},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.41110000014305115},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.39820000529289246},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3723999857902527},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3698999881744385},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33550000190734863},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.30230000615119934},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.2946000099182129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.289000004529953},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.26989999413490295}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2025.09.168","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.168","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2025.09.168","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2025.09.168","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4925423436","display_name":"\u975e\u7dda\u5f62\u7279\u5fb4\u91cf\u306b\u57fa\u3065\u304f\u65b0\u305f\u306a\u9ad8\u6b21\u5143\u7d71\u8a08\u7406\u8ad6\u306e\u958b\u767a\u3068\u305d\u306e\u5fdc\u7528","funder_award_id":"22K03412","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5676530573","display_name":"\u9ad8\u6b21\u5143\u5c0f\u6a19\u672c\u306b\u304a\u3051\u308b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u624b\u6cd5\u3068\u30ab\u30fc\u30cd\u30eb\u6cd5\u306e\u6709\u52b9\u6027\u306b\u95a2\u3059\u308b\u7406\u8ad6\u3068\u5fdc\u7528","funder_award_id":"24K20748","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7260458244","display_name":"Developments of statistical compression technology for massive data having tensor structures","funder_award_id":"22K19769","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7666424968","display_name":"Innovative Developments of Theories and Methodologies for Large Complex Data","funder_award_id":"20H00576","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320327572","display_name":"Promotion and Mutual Aid Corporation for Private Schools of Japan","ror":"https://ror.org/05h2t6865"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W317995076","https://openalex.org/W2007527993","https://openalex.org/W2071988253","https://openalex.org/W2100051950","https://openalex.org/W2325127698","https://openalex.org/W2904312908","https://openalex.org/W2959802665","https://openalex.org/W2988679481","https://openalex.org/W3167925194","https://openalex.org/W3188161751","https://openalex.org/W4220830728","https://openalex.org/W4388664589"],"related_works":[],"abstract_inverted_index":{"The":[0],"asymptotic":[1,26],"properties":[2,63,92],"of":[3,43,80,93,117],"k-means":[4,17,82,95],"clustering":[5,49,119],"in":[6,68,99],"high-dimensional":[7,36,69,100,125],"settings":[8,101],"have":[9],"been":[10],"studied":[11],"extensively.":[12],"Similarly,":[13],"research":[14,54],"on":[15,35],"kernel":[16,37,44,59,81,94,98],"has":[18,55],"shown":[19],"that":[20,40,57],"when":[21,83],"using":[22],"Gaussian":[23,66],"kernel,":[24],"its":[25],"behavior":[27,79],"remains":[28],"unchanged":[29],"from":[30],"conventional":[31],"k-means.":[32],"However,":[33],"studies":[34],"methods":[38],"suggest":[39],"the":[41],"choice":[42],"function":[45],"can":[46],"significantly":[47],"impact":[48],"performance.":[50],"For":[51],"instance,":[52],"previous":[53],"demonstrated":[56],"polynomial":[58,86,97],"functions":[60],"exhibit":[61],"distinct":[62],"compared":[64],"to":[65,77,113,124],"kernels":[67],"classification":[70],"tasks.":[71],"Given":[72],"this,":[73],"it":[74],"is":[75],"crucial":[76],"investigate":[78],"employing":[84],"a":[85,104,114],"kernel.":[87],"This":[88],"study":[89],"explores":[90],"theoretical":[91],"with":[96,107],"and":[102,121],"provides":[103],"comparative":[105],"analysis":[106],"existing":[108],"methods.":[109],"Our":[110],"findings":[111],"contribute":[112],"deeper":[115],"understanding":[116],"kernel-based":[118],"techniques":[120],"their":[122],"applicability":[123],"data.":[126]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-06T00:00:00"}