{"id":"https://openalex.org/W4361793652","doi":"https://doi.org/10.1145/3577530.3577557","title":"A Density Peak Clustering algorithm based on Adaptive K-nearest Neighbors with Evidential Strategy","display_name":"A Density Peak Clustering algorithm based on Adaptive K-nearest Neighbors with Evidential Strategy","publication_year":2022,"publication_date":"2022-12-09","ids":{"openalex":"https://openalex.org/W4361793652","doi":"https://doi.org/10.1145/3577530.3577557"},"language":"en","primary_location":{"id":"doi:10.1145/3577530.3577557","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3577530.3577557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence","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/A5041363743","display_name":"Fengjun Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I4387155233","display_name":"Shandong Electric Power Engineering Consulting Institute Corp","ror":"https://ror.org/052b58660","country_code":"CN","type":"company","lineage":["https://openalex.org/I4387155233"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fengjun Ji","raw_affiliation_strings":["Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China"],"affiliations":[{"raw_affiliation_string":"Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China","institution_ids":["https://openalex.org/I4387155233"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052775313","display_name":"Lin Li","orcid":"https://orcid.org/0000-0001-7564-1337"},"institutions":[{"id":"https://openalex.org/I4387155233","display_name":"Shandong Electric Power Engineering Consulting Institute Corp","ror":"https://ror.org/052b58660","country_code":"CN","type":"company","lineage":["https://openalex.org/I4387155233"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Li","raw_affiliation_strings":["Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China"],"affiliations":[{"raw_affiliation_string":"Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China","institution_ids":["https://openalex.org/I4387155233"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028395862","display_name":"Tao Zhang","orcid":"https://orcid.org/0000-0002-3728-2860"},"institutions":[{"id":"https://openalex.org/I4387155233","display_name":"Shandong Electric Power Engineering Consulting Institute Corp","ror":"https://ror.org/052b58660","country_code":"CN","type":"company","lineage":["https://openalex.org/I4387155233"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zhang","raw_affiliation_strings":["Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China"],"affiliations":[{"raw_affiliation_string":"Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China","institution_ids":["https://openalex.org/I4387155233"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061259139","display_name":"Bin Zhang","orcid":"https://orcid.org/0000-0003-4368-2067"},"institutions":[{"id":"https://openalex.org/I4387155233","display_name":"Shandong Electric Power Engineering Consulting Institute Corp","ror":"https://ror.org/052b58660","country_code":"CN","type":"company","lineage":["https://openalex.org/I4387155233"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Zhang","raw_affiliation_strings":["Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China"],"affiliations":[{"raw_affiliation_string":"Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China","institution_ids":["https://openalex.org/I4387155233"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067960019","display_name":"Junbo Yang","orcid":"https://orcid.org/0000-0002-7956-2828"},"institutions":[{"id":"https://openalex.org/I4387155233","display_name":"Shandong Electric Power Engineering Consulting Institute Corp","ror":"https://ror.org/052b58660","country_code":"CN","type":"company","lineage":["https://openalex.org/I4387155233"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junbo Yang","raw_affiliation_strings":["Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China"],"affiliations":[{"raw_affiliation_string":"Shandong Electric Power Engineering Consulting Institute Corp.,Ltd., China","institution_ids":["https://openalex.org/I4387155233"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090873546","display_name":"Jie Yin","orcid":"https://orcid.org/0000-0003-2708-0111"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Yin","raw_affiliation_strings":["Nanjing Ruisong Information Technology Co., Ltd., China"],"affiliations":[{"raw_affiliation_string":"Nanjing Ruisong Information Technology Co., Ltd., China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061408286","display_name":"Qian Wang","orcid":"https://orcid.org/0000-0001-9186-7985"},"institutions":[{"id":"https://openalex.org/I4210096899","display_name":"Jiangsu University of Science and Technology","ror":"https://ror.org/00tyjp878","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210096899"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Wang","raw_affiliation_strings":["Jiangsu University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Jiangsu University of Science and Technology, China","institution_ids":["https://openalex.org/I4210096899"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5041363743"],"corresponding_institution_ids":["https://openalex.org/I4387155233"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18565062,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"166","last_page":"171"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9993000030517578,"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":0.9993000030517578,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9914000034332275,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7841728925704956},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.678120493888855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6309509873390198},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5107653141021729},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5049760937690735},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4902694523334503},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4811445474624634},{"id":"https://openalex.org/keywords/cutoff","display_name":"Cutoff","score":0.4622400104999542},{"id":"https://openalex.org/keywords/nearest-neighbor-chain-algorithm","display_name":"Nearest-neighbor chain algorithm","score":0.42787033319473267},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42552223801612854},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4178583025932312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4051961302757263},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26273855566978455},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.21424829959869385},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.1870344579219818}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7841728925704956},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.678120493888855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6309509873390198},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5107653141021729},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5049760937690735},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4902694523334503},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4811445474624634},{"id":"https://openalex.org/C2778217198","wikidata":"https://www.wikidata.org/wiki/Q556977","display_name":"Cutoff","level":2,"score":0.4622400104999542},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.42787033319473267},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42552223801612854},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4178583025932312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4051961302757263},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26273855566978455},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.21424829959869385},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.1870344579219818},{"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/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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3577530.3577557","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3577530.3577557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1972098431","https://openalex.org/W1979679758","https://openalex.org/W2062458928","https://openalex.org/W2067483735","https://openalex.org/W2101231485","https://openalex.org/W2130473611","https://openalex.org/W2132377940","https://openalex.org/W2132904553","https://openalex.org/W2140405352","https://openalex.org/W2153233077","https://openalex.org/W2154579705","https://openalex.org/W2161160262","https://openalex.org/W2165835468","https://openalex.org/W2239301263","https://openalex.org/W2268194897","https://openalex.org/W2293435807","https://openalex.org/W2509842444","https://openalex.org/W2727530286","https://openalex.org/W2734337707","https://openalex.org/W2744207893","https://openalex.org/W2800478688","https://openalex.org/W2891176346","https://openalex.org/W3022254425","https://openalex.org/W3096902424","https://openalex.org/W3120740533","https://openalex.org/W4200349326","https://openalex.org/W4232436772","https://openalex.org/W4239881334","https://openalex.org/W4301347335"],"related_works":["https://openalex.org/W2519241726","https://openalex.org/W2375128115","https://openalex.org/W2362413895","https://openalex.org/W2368415703","https://openalex.org/W2391642054","https://openalex.org/W2797906284","https://openalex.org/W2182477562","https://openalex.org/W2162583212","https://openalex.org/W1671890395","https://openalex.org/W2169618946"],"abstract_inverted_index":{"A":[0],"novel":[1],"clustering":[2,70,189],"method":[3,71],"based":[4,72,121],"on":[5,10,73,122,163,204],"density":[6,32,68,103],"peaks(DPC)":[7],"was":[8],"published":[9],"the":[11,35,38,112,127,138,156],"journal":[12],"Science.":[13],"However,":[14],"for":[15,140],"DPC,":[16],"it":[17],"is":[18,58,152,195],"a":[19,45,56,67,94,100,119,149],"challenge":[20],"to":[21,92,125,154,171],"choose":[22],"an":[23,87],"appropriate":[24],"cutoff":[25,157],"distance":[26,158],"dc,":[27],"which":[28,97,151],"can":[29,98],"affect":[30],"local":[31,102],"distribution":[33,104],"of":[34,132],"datasets.":[36],"Additionally,":[37],"assigning":[39,79],"strategy":[40,80,91,120],"in":[41,160],"DPC":[42,177],"may":[43],"cause":[44],"domino":[46],"effect:":[47],"Many":[48],"following":[49],"points":[50],"might":[51],"be":[52],"wrongly":[53],"assigned":[54,59],"once":[55],"point":[57],"erroneously.":[60],"To":[61],"overcome":[62],"these":[63],"drawbacks,":[64],"we":[65,85,117],"introduce":[66,86,118],"peak":[69],"adaptive":[74,88],"K-nearest":[75,89],"neighbors":[76,90],"with":[77,175],"evidential":[78,123],"(named":[81],"as":[82,147,183],"AKE-DPC).":[83],"Firstly,":[84],"find":[93,106],"proper":[95],"K":[96],"form":[99],"reasonable":[101],"and":[105,143,166,178,186],"cluster":[107,113],"centers":[108,114],"simultaneously;":[109],"secondly,":[110],"after":[111],"are":[115,134,169],"found,":[116],"rules":[124],"assign":[126],"remaining":[128],"points.":[129],"The":[130,188],"advantages":[131],"AKE-DPC":[133],"obvious":[135],"that":[136,192],"negate":[137],"need":[139],"decision":[141],"graph":[142],"input":[144],"parameter":[145],"such":[146,182],"p,":[148],"percentage":[150],"used":[153],"computer":[155],"dc":[159],"DPC.":[161],"Experiments":[162],"both":[164,176],"synthetic":[165],"real-world":[167],"datasets":[168],"conducted":[170],"compare":[172],"our":[173,193],"algorithm":[174,194],"some":[179],"DPC-KNN":[180],"algorithms":[181,203],"ADPC-KNN,":[184],"FKNN-DPC":[185],"DPC-KNN.":[187],"results":[190],"demonstrate":[191],"not":[196],"only":[197],"effective":[198],"but":[199],"also":[200],"outperforms":[201],"other":[202],"most":[205],"cases.":[206]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}