{"id":"https://openalex.org/W4407832368","doi":"https://doi.org/10.1109/icdm59182.2024.00047","title":"ADOD: Adaptive Density Outlier Detection","display_name":"ADOD: Adaptive Density Outlier Detection","publication_year":2024,"publication_date":"2024-12-09","ids":{"openalex":"https://openalex.org/W4407832368","doi":"https://doi.org/10.1109/icdm59182.2024.00047"},"language":"en","primary_location":{"id":"doi:10.1109/icdm59182.2024.00047","doi":"https://doi.org/10.1109/icdm59182.2024.00047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm59182.2024.00047","pdf_url":null,"source":{"id":"https://openalex.org/S4210214883","display_name":"Proceedings","issn_l":"1550-4786","issn":["1550-4786","2374-8486"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5101658599","display_name":"Li Qian","orcid":"https://orcid.org/0009-0008-6384-4792","observed_orcids":["https://orcid.org/0009-0008-6384-4792","https://orcid.org/0000-0001-8951-1832"]},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Li Qian","raw_affiliation_strings":["Institute of Informatics, Ludwig Maximilian University of Munich,Munich,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Informatics, Ludwig Maximilian University of Munich,Munich,Germany","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004836994","display_name":"Jing Qian","orcid":"https://orcid.org/0000-0002-3085-8994","observed_orcids":["https://orcid.org/0000-0002-3085-8994"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing Qian","raw_affiliation_strings":["Carbon Neutral R&#x0026;D Center, China Railway Hi-Tech Industry Corporation Limited,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carbon Neutral R&#x0026;D Center, China Railway Hi-Tech Industry Corporation Limited,Beijing,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015182080","display_name":"Xin Sun","orcid":"https://orcid.org/0000-0003-1870-9037","observed_orcids":["https://orcid.org/0000-0003-1870-9037","https://orcid.org/0009-0000-4138-2162","https://orcid.org/0009-0007-1882-6117","https://orcid.org/0000-0001-9626-8623"]},"institutions":[{"id":"https://openalex.org/I6469544","display_name":"City University of Macau","ror":"https://ror.org/04gpd4q15","country_code":"MO","type":"education","lineage":["https://openalex.org/I6469544"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Xin Sun","raw_affiliation_strings":["City University of Macau,Faculty of Data Science,Macau,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University of Macau,Faculty of Data Science,Macau,China","institution_ids":["https://openalex.org/I6469544"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063545876","display_name":"Wengang Guo","orcid":"https://orcid.org/0009-0006-9822-9605","observed_orcids":["https://orcid.org/0009-0006-9822-9605"]},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wengang Guo","raw_affiliation_strings":["College of Electronic and Information Engineering, Tongji University,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic and Information Engineering, Tongji University,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062860517","display_name":"Christian B\u00f6hm","orcid":"https://orcid.org/0000-0002-2237-9969","observed_orcids":["https://orcid.org/0000-0002-2237-9969"]},"institutions":[{"id":"https://openalex.org/I129774422","display_name":"University of Vienna","ror":"https://ror.org/03prydq77","country_code":"AT","type":"education","lineage":["https://openalex.org/I129774422"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Christian B\u00f6hm","raw_affiliation_strings":["University of Vienna,Faculty of Computer Science,Vienna,Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Vienna,Faculty of Computer Science,Vienna,Austria","institution_ids":["https://openalex.org/I129774422"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9513,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78292466,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"400","last_page":"409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9980000257492065,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9980000257492065,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.0003000000142492354,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier-detection","display_name":"outlier detection","score":0.9929999709129333},{"id":"https://openalex.org/keywords/perplexity","display_name":"perplexity","score":0.777999997138977},{"id":"https://openalex.org/keywords/local-density-estimation","display_name":"local density estimation","score":0.9549999833106995},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.7229999899864197}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6106759309768677},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.595818042755127},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5912450551986694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3976224660873413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3476988971233368}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdm59182.2024.00047","doi":"https://doi.org/10.1109/icdm59182.2024.00047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm59182.2024.00047","pdf_url":null,"source":{"id":"https://openalex.org/S4210214883","display_name":"Proceedings","issn_l":"1550-4786","issn":["1550-4786","2374-8486"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"display_name":"Affordable and clean energy","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/7"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G422799748","display_name":null,"funder_award_id":"0006/2024/RIA1","funder_id":"https://openalex.org/F4320321655","funder_display_name":"Science and Technology Development Fund"}],"funders":[{"id":"https://openalex.org/F4320321655","display_name":"Science and Technology Development Fund","ror":"https://ror.org/044vr6g03"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1530232915","https://openalex.org/W1559250156","https://openalex.org/W1584412742","https://openalex.org/W1986332411","https://openalex.org/W2056081083","https://openalex.org/W2103914106","https://openalex.org/W2124509324","https://openalex.org/W2129281431","https://openalex.org/W2132870739","https://openalex.org/W2144182447","https://openalex.org/W2296719434","https://openalex.org/W2593864460","https://openalex.org/W2892035503","https://openalex.org/W2902415114","https://openalex.org/W2902758299","https://openalex.org/W2998702515","https://openalex.org/W3017320328","https://openalex.org/W3087668759","https://openalex.org/W3089028909","https://openalex.org/W3095320854","https://openalex.org/W3117098906","https://openalex.org/W3128465814","https://openalex.org/W3171259348","https://openalex.org/W3206604724","https://openalex.org/W3217458064","https://openalex.org/W4206916698","https://openalex.org/W4226060502","https://openalex.org/W4241492760","https://openalex.org/W4283810514","https://openalex.org/W4310276570","https://openalex.org/W4312433903","https://openalex.org/W4367016885","https://openalex.org/W6607543680","https://openalex.org/W6748816842","https://openalex.org/W6754841127","https://openalex.org/W6757844995"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Outlier":[0,59],"detection":[1,205],"plays":[2],"a":[3,52,87,127],"dual":[4],"role":[5],"in":[6],"data":[7,10,32,45,101,117,137],"analysis:":[8],"cleansing":[9],"to":[11,62,94,111,114,116,142],"optimize":[12],"the":[13,96,107,133,182,188,201],"performance":[14,41,184],"of":[15,31,69,84,99,185,203],"downstream":[16],"tasks":[17],"and":[18,40,78,131,139,162,166,172,195,207],"identifying":[19],"potentially":[20],"rare":[21],"valuable":[22],"events":[23],"or":[24],"patterns.":[25],"Proximity-based":[26],"methods,":[27],"which":[28],"are":[29,35],"independent":[30],"distribution":[33],"assumptions,":[34],"plagued":[36],"by":[37],"parameter":[38],"selection":[39],"challenges":[42],"when":[43],"handling":[44],"with":[46,118,169],"varying":[47,119],"densities.":[48,120],"This":[49,155,198],"study":[50,156,199],"proposed":[51],"novel":[53],"unsupervised":[54],"algorithm":[55],"named":[56],"Adaptive":[57],"Density":[58],"Detection":[60],"(ADOD)":[61],"address":[63],"these":[64],"challenges.":[65],"The":[66],"core":[67],"innovation":[68],"ADOD":[70,91,122,158],"involves":[71],"two":[72],"main":[73],"aspects:":[74],"adaptive":[75],"neighborhood":[76,108],"boundaries":[77,109],"density":[79,125,134],"consistency":[80],"scoring.":[81],"First,":[82],"instead":[83],"relying":[85],"on":[86,159],"predefined":[88],"fixed":[89],"radius,":[90],"employs":[92],"perplexity":[93],"calculate":[95],"local":[97,124],"scale":[98,113],"each":[100],"point.":[102],"It":[103],"then":[104],"dynamically":[105],"adjusts":[106],"according":[110],"this":[112],"adapt":[115],"Second,":[121],"estimates":[123],"using":[126],"mutual":[128],"neighbor":[129],"graph":[130],"combines":[132],"differences":[135],"between":[136],"points":[138],"their":[140,153,209],"neighbors":[141],"compute":[143],"outlier":[144,204],"scores,":[145],"effectively":[146],"distinguishing":[147],"outliers":[148],"that":[149],"significantly":[150],"deviate":[151],"from":[152,175],"surroundings.":[154],"evaluated":[157],"one":[160],"synthetic":[161],"32":[163],"real":[164],"datasets,":[165],"compared":[167],"it":[168],"14":[170],"classical":[171],"state-of-the-art":[173],"algorithms":[174],"different":[176],"categories.":[177],"Extensive":[178],"experimental":[179],"results":[180],"demonstrated":[181],"superior":[183],"ADOD,":[186],"achieving":[187],"highest":[189],"average":[190],"accuracy":[191],"across":[192],"ROC,":[193],"P@N,":[194],"AP":[196],"metrics.":[197],"promotes":[200],"development":[202],"techniques":[206],"expands":[208],"potential":[210],"for":[211],"real-time":[212],"applications.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-10-06T10:28:23.643775","created_date":"2025-10-10T00:00:00"}
