{"id":"https://openalex.org/W3078332959","doi":"https://doi.org/10.1109/iske47853.2019.9170434","title":"Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors","display_name":"Study on Density Peaks Clustering Based on Hierarchical K-Nearest Neighbors","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3078332959","doi":"https://doi.org/10.1109/iske47853.2019.9170434","mag":"3078332959"},"language":"en","primary_location":{"id":"doi:10.1109/iske47853.2019.9170434","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske47853.2019.9170434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","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/A5046665845","display_name":"Chunhua Ren","orcid":"https://orcid.org/0000-0002-8604-5458"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhua Ren","raw_affiliation_strings":["Southwest Jiaotong University,School of Information Science and Technology,Chengdu,China","School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Information Science and Technology,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102952970","display_name":"Linfu Sun","orcid":"https://orcid.org/0000-0002-3381-3548"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linfu Sun","raw_affiliation_strings":["Southwest Jiaotong University,School of Information Science and Technology,Chengdu,China","School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Information Science and Technology,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110050095","display_name":"Qishi Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qishi Wu","raw_affiliation_strings":["Southwest Jiaotong University,School of Information Science and Technology,Chengdu,China","School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,School of Information Science and Technology,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1446,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62651709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"664","last_page":"668"},"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9771000146865845,"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/T10057","display_name":"Face and Expression Recognition","score":0.9745000004768372,"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/dbscan","display_name":"DBSCAN","score":0.9001147747039795},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7966529726982117},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6529140472412109},{"id":"https://openalex.org/keywords/nearest-neighbor-chain-algorithm","display_name":"Nearest-neighbor chain algorithm","score":0.6484524011611938},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5905949473381042},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.5328001976013184},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48539814352989197},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4228758215904236},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4216092824935913},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3650299608707428},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.33008623123168945},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32088199257850647},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.26158416271209717},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.24064892530441284},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.1061636209487915}],"concepts":[{"id":"https://openalex.org/C46576248","wikidata":"https://www.wikidata.org/wiki/Q1114630","display_name":"DBSCAN","level":5,"score":0.9001147747039795},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7966529726982117},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6529140472412109},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.6484524011611938},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5905949473381042},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.5328001976013184},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48539814352989197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4228758215904236},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4216092824935913},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3650299608707428},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.33008623123168945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32088199257850647},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.26158416271209717},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.24064892530441284},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.1061636209487915},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iske47853.2019.9170434","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iske47853.2019.9170434","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1673310716","https://openalex.org/W2011430131","https://openalex.org/W2040628945","https://openalex.org/W2040888993","https://openalex.org/W2125836634","https://openalex.org/W2165232124","https://openalex.org/W2165835468","https://openalex.org/W2268194897","https://openalex.org/W2293435807","https://openalex.org/W2734337707","https://openalex.org/W2789456849","https://openalex.org/W6637131181"],"related_works":["https://openalex.org/W2394193399","https://openalex.org/W2519241726","https://openalex.org/W2054669762","https://openalex.org/W2375128115","https://openalex.org/W2387373525","https://openalex.org/W2809859387","https://openalex.org/W2362413895","https://openalex.org/W3134385809","https://openalex.org/W2368415703","https://openalex.org/W4220814143"],"abstract_inverted_index":{"Density":[0],"Peaks":[1],"Clustering":[2],"is":[3,35,49],"a":[4,21,39],"novel":[5],"clustering":[6,33],"algorithm,":[7],"which":[8,51],"can":[9],"find":[10],"clusters":[11],"of":[12,60],"arbitrary":[13],"shapes":[14],"with":[15,80],"fast":[16],"speed.":[17],"However,":[18],"it":[19],"has":[20,63],"few":[22],"disadvantages,":[23],"for":[24],"example,":[25],"when":[26],"the":[27,32,68,75],"data":[28,61,89],"are":[29],"unevenly":[30],"distributed,":[31],"performance":[34],"not":[36],"good.":[37],"Therefore,":[38],"improved":[40],"DPC":[41],"based":[42],"on":[43],"hierarchical":[44],"k-nearest":[45,53],"neighbor":[46],"(HKNN-DPC)":[47],"algorithm":[48,77],"proposed,":[50],"divided":[52],"neighbors":[54],"into":[55],"three":[56],"layers,":[57],"each":[58],"layer":[59],"points":[62],"different":[64],"weight,":[65],"and":[66,83,87],"redesigned":[67],"local":[69],"density":[70],"calculation":[71],"approach.":[72],"We":[73],"adopt":[74],"proposed":[76],"to":[78],"compare":[79],"DPC,":[81],"DBSCAN":[82],"K-means":[84],"in":[85],"synthesized":[86],"UCI":[88],"sets.":[90],"The":[91],"experimental":[92],"results":[93],"indicated":[94],"HKNN-DPC":[95],"had":[96],"better":[97],"performance.":[98]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}