{"id":"https://openalex.org/W4285220743","doi":"https://doi.org/10.1109/tkde.2022.3172167","title":"A High-dimensional Outlier Detection Approach Based on Local Coulomb Force","display_name":"A High-dimensional Outlier Detection Approach Based on Local Coulomb Force","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285220743","doi":"https://doi.org/10.1109/tkde.2022.3172167"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2022.3172167","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3172167","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-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/A5054997322","display_name":"Pengyun Zhu","orcid":"https://orcid.org/0000-0002-2637-2908"},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengyun Zhu","raw_affiliation_strings":["School of computer Science and Technology, Taiyuan University of Science and Technology, 117763 Taiyuan, Shanxi, China","School of Computer Science and Technology, Taiyuan University of Science and Technology (TYUST), Taiyuan, Shanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of computer Science and Technology, Taiyuan University of Science and Technology, 117763 Taiyuan, Shanxi, China","institution_ids":["https://openalex.org/I46305995"]},{"raw_affiliation_string":"School of Computer Science and Technology, Taiyuan University of Science and Technology (TYUST), Taiyuan, Shanxi, China","institution_ids":["https://openalex.org/I46305995"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101769882","display_name":"Chaowei Zhang","orcid":"https://orcid.org/0000-0001-5051-5318"},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaowei Zhang","raw_affiliation_strings":["Computer science and technology, Yangzhou University, 38043 Yangzhou, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer science and technology, Yangzhou University, 38043 Yangzhou, Jiangsu, China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100347688","display_name":"Xiaofeng Li","orcid":"https://orcid.org/0000-0002-4115-3287"},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaofeng Li","raw_affiliation_strings":["School of Computer Science and Technology, Taiyuan University of Science and Technology, 117763 Taiyuan, Shanxi, China","School of Computer Science and Technology, Taiyuan University of Science and Technology (TYUST), Taiyuan, Shanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Taiyuan University of Science and Technology, 117763 Taiyuan, Shanxi, China","institution_ids":["https://openalex.org/I46305995"]},{"raw_affiliation_string":"School of Computer Science and Technology, Taiyuan University of Science and Technology (TYUST), Taiyuan, Shanxi, China","institution_ids":["https://openalex.org/I46305995"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046188958","display_name":"Jifu Zhang","orcid":"https://orcid.org/0000-0002-0396-8901"},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jifu Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Taiyuan University of Science and Technology, 117763 Taiyuan, Shanxi, China","School of Computer Science and Technology, Taiyuan University of Science and Technology (TYUST), Taiyuan, Shanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Taiyuan University of Science and Technology, 117763 Taiyuan, Shanxi, China","institution_ids":["https://openalex.org/I46305995"]},{"raw_affiliation_string":"School of Computer Science and Technology, Taiyuan University of Science and Technology (TYUST), Taiyuan, Shanxi, China","institution_ids":["https://openalex.org/I46305995"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042766429","display_name":"Xiao Qin","orcid":"https://orcid.org/0000-0002-8345-3587"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao Qin","raw_affiliation_strings":["Computer Science and Software Engineering, Auburn University, 1383 Auburn, Alabama, United States","Department of Computer Science and Software Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Software Engineering, Auburn University, 1383 Auburn, Alabama, United States","institution_ids":["https://openalex.org/I82497590"]},{"raw_affiliation_string":"Department of Computer Science and Software Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, USA","institution_ids":["https://openalex.org/I82497590"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7555,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.91523266,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9718000292778015,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9624999761581421,"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/outlier","display_name":"Outlier","score":0.8536584377288818},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8264087438583374},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5829726457595825},{"id":"https://openalex.org/keywords/local-outlier-factor","display_name":"Local outlier factor","score":0.5739559531211853},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5557090640068054},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5294088125228882},{"id":"https://openalex.org/keywords/coulomb","display_name":"Coulomb","score":0.5156399011611938},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5071356296539307},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4730897545814514},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4716412425041199},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4497690498828888},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43852242827415466},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4297507107257843},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.42299336194992065},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26583433151245117},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2460525631904602},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.16732999682426453}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8536584377288818},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8264087438583374},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5829726457595825},{"id":"https://openalex.org/C169029474","wikidata":"https://www.wikidata.org/wiki/Q387942","display_name":"Local outlier factor","level":3,"score":0.5739559531211853},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5557090640068054},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5294088125228882},{"id":"https://openalex.org/C9342510","wikidata":"https://www.wikidata.org/wiki/Q25406","display_name":"Coulomb","level":3,"score":0.5156399011611938},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5071356296539307},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4730897545814514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4716412425041199},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4497690498828888},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43852242827415466},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4297507107257843},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.42299336194992065},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26583433151245117},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2460525631904602},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.16732999682426453},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"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/C147120987","wikidata":"https://www.wikidata.org/wiki/Q2225","display_name":"Electron","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2022.3172167","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2022.3172167","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7599999904632568}],"awards":[{"id":"https://openalex.org/G3989319793","display_name":null,"funder_award_id":"62172293","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W199035648","https://openalex.org/W228527914","https://openalex.org/W845124934","https://openalex.org/W1552339598","https://openalex.org/W1986332411","https://openalex.org/W1992269475","https://openalex.org/W2006820260","https://openalex.org/W2007082564","https://openalex.org/W2015887370","https://openalex.org/W2085766370","https://openalex.org/W2090197195","https://openalex.org/W2097017789","https://openalex.org/W2117672932","https://openalex.org/W2155653793","https://openalex.org/W2168532736","https://openalex.org/W2169153112","https://openalex.org/W2276652386","https://openalex.org/W2336788611","https://openalex.org/W2590761734","https://openalex.org/W2611477785","https://openalex.org/W2613430468","https://openalex.org/W2728134755","https://openalex.org/W2892398836","https://openalex.org/W2896347308","https://openalex.org/W2917198203","https://openalex.org/W2954482441","https://openalex.org/W2955356027","https://openalex.org/W2961839652","https://openalex.org/W2994497769","https://openalex.org/W3007537043","https://openalex.org/W3024017681","https://openalex.org/W3035622304","https://openalex.org/W3043844898","https://openalex.org/W3091873932","https://openalex.org/W3113874980","https://openalex.org/W3128944770","https://openalex.org/W4251731432","https://openalex.org/W4254182148","https://openalex.org/W6632829031","https://openalex.org/W6703470281","https://openalex.org/W6765489981"],"related_works":["https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W114119537","https://openalex.org/W2912112202","https://openalex.org/W2761705761","https://openalex.org/W2770832849","https://openalex.org/W4240627425","https://openalex.org/W205872183","https://openalex.org/W2019014808","https://openalex.org/W2946096271"],"abstract_inverted_index":{"Traditional":[0],"outlier":[1,39,43,100,109,128,163],"detections":[2],"are":[3,169],"inadequate":[4],"for":[5],"high-dimensional":[6,31,108,162],"data":[7,32,57,67,85,89,164],"analysis":[8],"due":[9],"to":[10,16],"the":[11,24,104,138,148,152,167],"interference":[12,153],"of":[13,20,38,66,84,106,127,154,156],"distance":[14],"tending":[15],"be":[17],"concentrated":[18],"(\u201ccurse":[19],"dimensionality\u201d).":[21],"Inspired":[22],"by":[23,69,166],"Coulomb\u2019s":[25],"law,":[26],"we":[27,131],"propose":[28],"a":[29,88,97,107,118],"new":[30,98],"similarity":[33,54],"measure":[34,82],"vector,":[35],"which":[36,102],"consists":[37],"Coulomb":[40,44,48,77],"force":[41,49,79],"and":[42,133,140],"resultant":[45,78],"force.":[46],"Outlier":[47],"not":[50],"only":[51],"effectively":[52,81,150],"gauges":[53],"measures":[55],"among":[56,64],"objects,":[58],"but":[59],"also":[60],"fully":[61],"reflects":[62],"differences":[63],"dimensions":[65],"objects":[68,86],"vector":[70],"projection":[71],"in":[72],"each":[73],"dimension.":[74],"More":[75],"importantly,":[76],"can":[80],"deviations":[83],"from":[87],"center,":[90],"making":[91],"detection":[92,110],"results":[93,145],"interpretable.":[94,170],"We":[95],"introduce":[96],"neighborhood":[99],"factor,":[101],"drives":[103],"development":[105],"algorithm.":[111],"In":[112],"our":[113,135],"approach,":[114],"attribute":[115],"values":[116],"with":[117],"high":[119],"deviation":[120],"degree":[121],"is":[122],"treated":[123],"as":[124],"interpretable":[125],"information":[126],"data.":[129],"Finally,":[130],"implement":[132],"evaluate":[134],"algorithm":[136,149,168],"using":[137],"UCI":[139],"synthetic":[141],"datasets.":[142],"Our":[143],"experimental":[144],"show":[146],"that":[147,161],"alleviates":[151],"\u201cCurse":[155],"Dimensionality\u201d.":[157],"The":[158],"findings":[159],"confirm":[160],"originated":[165]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}