{"id":"https://openalex.org/W2998572005","doi":"https://doi.org/10.1109/icpics47731.2019.8942511","title":"A Density-based Clustering Algorithm Using Adaptive Parameter K-Reverse Nearest Neighbor","display_name":"A Density-based Clustering Algorithm Using Adaptive Parameter K-Reverse Nearest Neighbor","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2998572005","doi":"https://doi.org/10.1109/icpics47731.2019.8942511","mag":"2998572005"},"language":"en","primary_location":{"id":"doi:10.1109/icpics47731.2019.8942511","doi":"https://doi.org/10.1109/icpics47731.2019.8942511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpics47731.2019.8942511","pdf_url":null,"source":{"id":"https://openalex.org/S4306498680","display_name":"IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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":null,"display_name":"Pengyu Pei","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengyu Pei","raw_affiliation_strings":["Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115603674","display_name":"Dong Zhang","orcid":"https://orcid.org/0000-0002-8948-2856","observed_orcids":["https://orcid.org/0000-0002-8948-2856"]},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Zhang","raw_affiliation_strings":["System Software R&D Department, Inspur Electronic Information Industry Co., Ltd, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"System Software R&D Department, Inspur Electronic Information Industry Co., Ltd, Zhengzhou, China","institution_ids":["https://openalex.org/I4210144143"]}]},{"author_position":"last","author":{"id":null,"display_name":"Feng Guo","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Guo","raw_affiliation_strings":["System Software R&D Department, Inspur Electronic Information Industry Co., Ltd, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"System Software R&D Department, Inspur Electronic Information Industry Co., Ltd, Zhengzhou, China","institution_ids":["https://openalex.org/I4210144143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.569,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.70576109,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":"41.6","issue":null,"first_page":"455","last_page":"458"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":1.0,"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":1.0,"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/T11106","display_name":"Data Management and Algorithms","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.8080000281333923},{"id":"https://openalex.org/keywords/density-based-clustering","display_name":"density-based clustering","score":0.9909999966621399},{"id":"https://openalex.org/keywords/adaptive-parameter-selection","display_name":"adaptive parameter selection","score":0.7310000061988831},{"id":"https://openalex.org/keywords/cluster-density","display_name":"cluster density","score":0.46000000834465027},{"id":"https://openalex.org/keywords/cluster-separation","display_name":"cluster separation","score":0.6200000047683716},{"id":"https://openalex.org/keywords/parameter-free-clustering","display_name":"parameter-free clustering","score":0.7350000143051147}],"concepts":[{"id":"https://openalex.org/C46576248","wikidata":"https://www.wikidata.org/wiki/Q1114630","display_name":"DBSCAN","level":5,"score":0.9577759504318237},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8498642444610596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6357378363609314},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.6301214098930359},{"id":"https://openalex.org/C102164700","wikidata":"https://www.wikidata.org/wiki/Q17162702","display_name":"Nearest-neighbor chain algorithm","level":5,"score":0.5788727402687073},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5729787349700928},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5617555975914001},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5499401688575745},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.527531623840332},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4607059061527252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41011911630630493},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38383233547210693}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpics47731.2019.8942511","doi":"https://doi.org/10.1109/icpics47731.2019.8942511","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpics47731.2019.8942511","pdf_url":null,"source":{"id":"https://openalex.org/S4306498680","display_name":"IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W8722571","https://openalex.org/W1673310716","https://openalex.org/W1992419399","https://openalex.org/W2026453187","https://openalex.org/W2064314060","https://openalex.org/W2118211060","https://openalex.org/W2118862812","https://openalex.org/W2165835468"],"related_works":["https://openalex.org/W2901438737","https://openalex.org/W2967391214","https://openalex.org/W2779640815","https://openalex.org/W2903986098","https://openalex.org/W2798532996","https://openalex.org/W3195647032","https://openalex.org/W7140168318","https://openalex.org/W2161840603","https://openalex.org/W3124081135","https://openalex.org/W6908327716"],"abstract_inverted_index":{"Density-based":[0],"clustering":[1,11,41,51,100],"method":[2],"DBSCAN":[3,32],"is":[4,94],"one":[5],"of":[6,22,28,79],"the":[7,20,40,72,75,80,83],"most":[8],"widely":[9],"used":[10],"methods,":[12],"this":[13,92],"algorithm":[14,33,52,93],"does":[15],"not":[16],"need":[17],"to":[18],"input":[19,86],"number":[21],"clustering,":[23],"and":[24,64],"can":[25,61],"find":[26],"clusters":[27,66],"any":[29],"shape.":[30],"However,":[31],"requires":[34],"two":[35],"parameters,":[36,39],"with":[37,67],"different":[38,68],"results":[42,89],"are":[43],"quite":[44],"different.":[45],"This":[46],"paper":[47],"proposes":[48],"a":[49],"density-based":[50,99],"using":[53,102],"adaptive":[54,103],"parameter":[55],"k-reverse":[56],"nearest":[57],"neighbor:":[58],"ARKNN-DBSCAN,":[59],"which":[60],"effectively":[62],"identify":[63],"separate":[65],"densities":[69],"by":[70],"analyzing":[71],"correlation":[73],"through":[74],"reverse":[76],"k-nearest":[77],"neighbor":[78],"observation":[81],"in":[82],"dataset":[84],"without":[85],"parameters.":[87],"Our":[88],"show":[90],"that":[91],"more":[95],"accurate":[96],"than":[97],"other":[98],"algorithms":[101],"parameter.":[104]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-01-10T00:00:00"}