{"id":"https://openalex.org/W4413868058","doi":"https://doi.org/10.3389/fams.2025.1598165","title":"Density peak clustering algorithm based on weighted mutual K-nearest neighbors","display_name":"Density peak clustering algorithm based on weighted mutual K-nearest neighbors","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4413868058","doi":"https://doi.org/10.3389/fams.2025.1598165"},"language":"en","primary_location":{"id":"doi:10.3389/fams.2025.1598165","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fams.2025.1598165","pdf_url":"https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1598165/pdf","source":{"id":"https://openalex.org/S2597085352","display_name":"Frontiers in Applied Mathematics and Statistics","issn_l":"2297-4687","issn":["2297-4687"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Applied Mathematics and Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1598165/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046665845","display_name":"Chunhua Ren","orcid":"https://orcid.org/0000-0002-8604-5458"},"institutions":[{"id":"https://openalex.org/I4210131342","display_name":"Yibin University","ror":"https://ror.org/03w8m2977","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131342"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhua Ren","raw_affiliation_strings":["School of Computer Science and Technology, Yibin University, Yibin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Yibin University, Yibin, China","institution_ids":["https://openalex.org/I4210131342"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007621167","display_name":"Chaorong Li","orcid":"https://orcid.org/0000-0001-8336-2661"},"institutions":[{"id":"https://openalex.org/I4210131342","display_name":"Yibin University","ror":"https://ror.org/03w8m2977","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131342"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chaorong Li","raw_affiliation_strings":["School of Computer Science and Technology, Yibin University, Yibin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Yibin University, Yibin, China","institution_ids":["https://openalex.org/I4210131342"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111972593","display_name":"Yang Yu","orcid":"https://orcid.org/0000-0002-0784-9715"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yu","raw_affiliation_strings":["School of Computer and Software, Southwest Petroleum University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Software, Southwest Petroleum University, Chengdu, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101866598","display_name":"Wanan Yang","orcid":"https://orcid.org/0000-0001-7887-6676"},"institutions":[{"id":"https://openalex.org/I4210131342","display_name":"Yibin University","ror":"https://ror.org/03w8m2977","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131342"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanan Yang","raw_affiliation_strings":["School of Computer Science and Technology, Yibin University, Yibin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Yibin University, Yibin, China","institution_ids":["https://openalex.org/I4210131342"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085105148","display_name":"Ruiqi Guo","orcid":"https://orcid.org/0000-0003-4729-7385"},"institutions":[{"id":"https://openalex.org/I4210131342","display_name":"Yibin University","ror":"https://ror.org/03w8m2977","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131342"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiqi Guo","raw_affiliation_strings":["School of Computer Science and Technology, Yibin University, Yibin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Yibin University, Yibin, China","institution_ids":["https://openalex.org/I4210131342"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007621167"],"corresponding_institution_ids":["https://openalex.org/I4210131342"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":1.396,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85670835,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"11","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9987000226974487,"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":0.9987000226974487,"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":0.9927999973297119,"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"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9765999913215637,"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":0.6352133750915527},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5853675603866577},{"id":"https://openalex.org/keywords/nearest-neighbor-chain-algorithm","display_name":"Nearest-neighbor chain algorithm","score":0.5180683135986328},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49738338589668274},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4708395004272461},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4356403052806854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3874639868736267},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3862037658691406},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.21712735295295715},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.2157745659351349}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6352133750915527},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5853675603866577},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.5180683135986328},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49738338589668274},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4708395004272461},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4356403052806854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3874639868736267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3862037658691406},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.21712735295295715},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.2157745659351349}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3389/fams.2025.1598165","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fams.2025.1598165","pdf_url":"https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1598165/pdf","source":{"id":"https://openalex.org/S2597085352","display_name":"Frontiers in Applied Mathematics and Statistics","issn_l":"2297-4687","issn":["2297-4687"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Applied Mathematics and Statistics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:46b890f9f68d42219d612847833cddff","is_oa":true,"landing_page_url":"https://doaj.org/article/46b890f9f68d42219d612847833cddff","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Applied Mathematics and Statistics, Vol 11 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fams.2025.1598165","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fams.2025.1598165","pdf_url":"https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1598165/pdf","source":{"id":"https://openalex.org/S2597085352","display_name":"Frontiers in Applied Mathematics and Statistics","issn_l":"2297-4687","issn":["2297-4687"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Applied Mathematics and Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413868058.pdf","grobid_xml":"https://content.openalex.org/works/W4413868058.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W2021101795","https://openalex.org/W2054564636","https://openalex.org/W2088252378","https://openalex.org/W2162833336","https://openalex.org/W2165232124","https://openalex.org/W2165835468","https://openalex.org/W2268194897","https://openalex.org/W2293435807","https://openalex.org/W2557166674","https://openalex.org/W2563889304","https://openalex.org/W2635535303","https://openalex.org/W2734337707","https://openalex.org/W2740924709","https://openalex.org/W2743418339","https://openalex.org/W2767506895","https://openalex.org/W2789456849","https://openalex.org/W2797273848","https://openalex.org/W2900089459","https://openalex.org/W2907673831","https://openalex.org/W2921078400","https://openalex.org/W2940963287","https://openalex.org/W2949034001","https://openalex.org/W2963961044","https://openalex.org/W2987721216","https://openalex.org/W2990584864","https://openalex.org/W3012210245","https://openalex.org/W3034656200","https://openalex.org/W3038505635","https://openalex.org/W3080634081","https://openalex.org/W3120683583","https://openalex.org/W3123053997","https://openalex.org/W3138778472","https://openalex.org/W3203644918","https://openalex.org/W4210719132","https://openalex.org/W4211194210","https://openalex.org/W4214817073","https://openalex.org/W4228998843","https://openalex.org/W4235145261","https://openalex.org/W4309047333","https://openalex.org/W4313593673","https://openalex.org/W4320181904","https://openalex.org/W4383736492","https://openalex.org/W4385737766","https://openalex.org/W4387122021","https://openalex.org/W4387807889","https://openalex.org/W4393950901"],"related_works":["https://openalex.org/W2519241726","https://openalex.org/W2375128115","https://openalex.org/W2362413895","https://openalex.org/W2368415703","https://openalex.org/W2101883792","https://openalex.org/W2391642054","https://openalex.org/W2147397890","https://openalex.org/W2797906284","https://openalex.org/W2182477562","https://openalex.org/W2062957446"],"abstract_inverted_index":{"Ever":[0],"since":[1],"Density":[2],"Peak":[3],"Clustering":[4],"(DPC)":[5],"was":[6],"published":[7],"in":[8,17,47,98,130,219],"Science,":[9],"it":[10,38],"has":[11,30,229],"been":[12,230],"widely":[13],"favored":[14],"and":[15,24,119,175,178,242],"applied":[16],"various":[18],"fields":[19],"due":[20,73],"to":[21,40,65,70,74],"its":[22,57],"concise":[23],"efficient":[25,202],"computational":[26],"theory.":[27],"However,":[28],"DPC":[29],"two":[31,103,159],"major":[32],"flaws.":[33],"On":[34,53],"the":[35,54,109,125,162,180,206,212,253],"one":[36],"hand,":[37,56],"fails":[39],"find":[41],"cluster":[42,128],"centers":[43,129],"of":[44,111,127,165,183,209,214],"low-density":[45],"clusters":[46,131],"datasets":[48],"with":[49,132],"uneven":[50,133],"density":[51,84,134,140],"distribution.":[52],"other":[55],"single":[58],"assignment":[59,149,164,203],"strategy,":[60],"which":[61,157],"only":[62],"assigns":[63],"points":[64,148,167,185,196],"high-density":[66],"clusters,":[67],"can":[68],"lead":[69],"incorrect":[71],"clustering":[72,86],"a":[75,82,137,146,216],"chain":[76],"reaction.":[77],"To":[78],"address":[79],"these":[80],"weaknesses,":[81],"new":[83,138],"peak":[85],"algorithm":[87,228,251],"based":[88,151,188,204,222],"on":[89,152,189,205,223,233,255],"weighted":[90,153,190],"mutual":[91,112,154,172,191],"K-nearest":[92,113,117,121,155,173,192,224],"neighbors":[93,114,118,174,193],"called":[94],"WMKNNDPC":[95,101,227],"is":[96,168,186],"proposed":[97],"this":[99],"paper.":[100],"offers":[102],"significant":[104],"advantages:":[105],"(1)":[106],"It":[107,144],"introduces":[108],"concept":[110],"by":[115,170],"using":[116,215],"inverse":[120],"neighbors,":[122,156],"allowing":[123],"for":[124,194,201],"identification":[126],"distribution":[135,208],"through":[136],"local":[139,207],"calculation":[141],"method.":[142],"(2)":[143],"includes":[145],"remaining":[147,195],"method":[150,199],"involves":[158],"stages:":[160],"first,":[161],"initial":[163],"data":[166,184],"done":[169],"combining":[171],"breadth-first":[176],"search,":[177],"second,":[179],"membership":[181],"degree":[182],"calculated":[187],"assignment.":[197],"This":[198],"allows":[200],"points,":[210],"avoiding":[211],"disadvantages":[213],"fixed":[217],"K-value":[218],"DPC-derived":[220],"algorithms":[221],"neighbors.":[225],"The":[226,245],"extensively":[231],"tested":[232],"two-dimensional":[234],"synthetic":[235],"datasets,":[236,238],"real":[237],"facial":[239],"recognition":[240],"dataset":[241],"parameter":[243],"analysis.":[244],"experimental":[246],"results":[247],"indicate":[248],"that":[249],"our":[250],"performs":[252],"best":[254],"most":[256],"datasets.":[257]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
