{"id":"https://openalex.org/W2515781906","doi":"https://doi.org/10.1145/2905055.2905201","title":"An Integrated Method for Outlier Detection with Analytical Study of Distance Based and Angle Based Approaches","display_name":"An Integrated Method for Outlier Detection with Analytical Study of Distance Based and Angle Based Approaches","publication_year":2016,"publication_date":"2016-03-04","ids":{"openalex":"https://openalex.org/W2515781906","doi":"https://doi.org/10.1145/2905055.2905201","mag":"2515781906"},"language":"en","primary_location":{"id":"doi:10.1145/2905055.2905201","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2905055.2905201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies","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/A5070633230","display_name":"Deepti Mishra","orcid":"https://orcid.org/0000-0001-5796-6728"},"institutions":[{"id":"https://openalex.org/I4210086299","display_name":"Noida International University","ror":"https://ror.org/001amd982","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210086299"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Deepti Mishra","raw_affiliation_strings":["Department of CSE, Noida International University, India"],"affiliations":[{"raw_affiliation_string":"Department of CSE, Noida International University, India","institution_ids":["https://openalex.org/I4210086299"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069086313","display_name":"Devpriya Soni","orcid":"https://orcid.org/0000-0001-5589-5962"},"institutions":[{"id":"https://openalex.org/I154970844","display_name":"Jaypee Institute of Information Technology","ror":"https://ror.org/05sttyy11","country_code":"IN","type":"education","lineage":["https://openalex.org/I154970844"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Devpriya Soni","raw_affiliation_strings":["Department of CSE and IT, JIIT, Noida, India"],"affiliations":[{"raw_affiliation_string":"Department of CSE and IT, JIIT, Noida, India","institution_ids":["https://openalex.org/I154970844"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5070633230"],"corresponding_institution_ids":["https://openalex.org/I4210086299"],"apc_list":null,"apc_paid":null,"fwci":0.89,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.84297359,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9905999898910522,"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"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9876999855041504,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.8561402559280396},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7449936866760254},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.7047429084777832},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6841593384742737},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6353238821029663},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6244521141052246},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5040608644485474},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.4989748001098633},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4915390908718109},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.48980212211608887},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42948710918426514},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42737525701522827},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4181489944458008},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22314521670341492},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1739656627178192}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8561402559280396},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7449936866760254},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.7047429084777832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6841593384742737},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6353238821029663},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6244521141052246},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5040608644485474},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.4989748001098633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4915390908718109},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.48980212211608887},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42948710918426514},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42737525701522827},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4181489944458008},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22314521670341492},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1739656627178192},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2905055.2905201","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2905055.2905201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies","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":25,"referenced_works":["https://openalex.org/W143395089","https://openalex.org/W208128215","https://openalex.org/W364522613","https://openalex.org/W1835248276","https://openalex.org/W1916943134","https://openalex.org/W1985123706","https://openalex.org/W1986332411","https://openalex.org/W2012444665","https://openalex.org/W2012833704","https://openalex.org/W2041559880","https://openalex.org/W2067785713","https://openalex.org/W2095897464","https://openalex.org/W2121659641","https://openalex.org/W2140190241","https://openalex.org/W2140413822","https://openalex.org/W2148610006","https://openalex.org/W2170902455","https://openalex.org/W2319459678","https://openalex.org/W2331052961","https://openalex.org/W2339703725","https://openalex.org/W2368081778","https://openalex.org/W4285719527","https://openalex.org/W6678015680","https://openalex.org/W6703697656","https://openalex.org/W6986189286"],"related_works":["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/W294405749","https://openalex.org/W4390662392","https://openalex.org/W71955863","https://openalex.org/W2359185137"],"abstract_inverted_index":{"In":[0,75],"this":[1,76,204],"era":[2],"detection":[3,92],"of":[4,13,36,46,119,127,137,162,180,190],"Outlier":[5],"is":[6,42,145,158],"a":[7,82,146,151],"significant":[8],"area":[9],"in":[10,72,176,198],"the":[11,73,117,124,128,135,177,188,199],"field":[12],"data":[14,37,47,95,141,200],"mining.":[15],"Outliers":[16],"can":[17,50,61],"be":[18,32,51,171],"generated":[19,33,64],"from":[20,38],"various":[21,194],"sources":[22],"like":[23,107],"manual":[24],"error,":[25,27],"system":[26],"mechanical":[28],"faults,":[29],"or":[30,56,110],"may":[31],"during":[34],"capturing":[35],"different":[39],"sources.":[40],"It":[41,60,113],"an":[43,159],"important":[44],"concept":[45,102],"mining":[48],"which":[49,157],"detected":[52],"by":[53],"using":[54],"supervised":[55],"unsupervised":[57],"learning":[58],"techniques.":[59],"detect":[62,174],"outlier":[63,91,98,195],"along":[65],"with":[66],"required":[67],"information":[68],"that":[69],"creates":[70],"noise":[71],"data.":[74,129,181],"paper":[77],"we":[78],"intend":[79],"to":[80,173,202],"present":[81],"comparative":[83],"study":[84,183],"between":[85,139],"distance":[86,105,109,165],"based":[87,90,101,131,166],"and":[88,143,164,193,201],"angle":[89,163],"methods":[93,106],"over":[94],"sets":[96],"for":[97,206],"detection.":[99],"Distance":[100],"uses":[103],"some":[104],"Euclidean":[108],"Manhattan":[111],"distance.":[112],"not":[114],"only":[115],"requires":[116],"understanding":[118],"mathematical":[120],"properties":[121],"but":[122],"also":[123,184],"relevant":[125],"knowledge":[126],"Angle":[130],"approach":[132,153],"emphasizes":[133],"on":[134],"deviation":[136],"angles":[138],"two":[140],"points":[142],"it":[144],"parameter":[147],"free":[148],"approach.":[149],"Further":[150],"new":[152],"has":[154],"been":[155],"introduced":[156],"integrated":[160],"method":[161,169],"approaches.":[167],"This":[168,182],"would":[170],"used":[172],"outliers":[175],"given":[178],"set":[179],"aims":[185],"at":[186],"providing":[187],"review":[189],"clustering":[191],"technique":[192],"analysis":[196],"techniques":[197],"use":[203],"comparison":[205],"further":[207],"research":[208],"studies.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
