{"id":"https://openalex.org/W2810004579","doi":"https://doi.org/10.1109/fskd.2017.8393023","title":"Based local density trajectory outlier detection with partition-and-detect framework","display_name":"Based local density trajectory outlier detection with partition-and-detect framework","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2810004579","doi":"https://doi.org/10.1109/fskd.2017.8393023","mag":"2810004579"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393023","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393023","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5015741034","display_name":"Fangjun Luan","orcid":"https://orcid.org/0000-0002-1833-3909"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fangjun Luan","raw_affiliation_strings":["Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100669699","display_name":"Yunting Zhang","orcid":"https://orcid.org/0009-0008-4169-3946"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunting Zhang","raw_affiliation_strings":["Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008988216","display_name":"Keyan Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keyan Cao","raw_affiliation_strings":["Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101901030","display_name":"Qi Li","orcid":"https://orcid.org/0000-0001-5938-0173"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Li","raw_affiliation_strings":["Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China","institution_ids":["https://openalex.org/I83714178"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5015741034"],"corresponding_institution_ids":["https://openalex.org/I83714178"],"apc_list":null,"apc_paid":null,"fwci":0.39,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.73030952,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1708","last_page":"1714"},"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9696000218391418,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8373925685882568},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.8067681789398193},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.6595844030380249},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6138102412223816},{"id":"https://openalex.org/keywords/local-outlier-factor","display_name":"Local outlier factor","score":0.6061348915100098},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5806630253791809},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4973011314868927},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.47066864371299744},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.41310808062553406},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32896173000335693},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2935583293437958}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8373925685882568},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8067681789398193},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.6595844030380249},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6138102412223816},{"id":"https://openalex.org/C169029474","wikidata":"https://www.wikidata.org/wiki/Q387942","display_name":"Local outlier factor","level":3,"score":0.6061348915100098},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5806630253791809},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4973011314868927},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.47066864371299744},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.41310808062553406},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32896173000335693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2935583293437958},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393023","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393023","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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":10,"referenced_works":["https://openalex.org/W1592237688","https://openalex.org/W2014944622","https://openalex.org/W2046466133","https://openalex.org/W2061240327","https://openalex.org/W2110784166","https://openalex.org/W2140190241","https://openalex.org/W4253461361","https://openalex.org/W4254182148","https://openalex.org/W4300363951","https://openalex.org/W6635480988"],"related_works":["https://openalex.org/W4313269841","https://openalex.org/W2022185900","https://openalex.org/W2046466133","https://openalex.org/W2363453073","https://openalex.org/W2181423736","https://openalex.org/W2287958981","https://openalex.org/W2810558525","https://openalex.org/W2810004579","https://openalex.org/W3032925585","https://openalex.org/W1507654104"],"abstract_inverted_index":{"In":[0,158],"this":[1],"paper,":[2],"with":[3,48,86,205],"the":[4,22,25,34,56,60,77,92,101,111,127,136,143,149,153,156,159,164,172,175,178,182,185,201,219,222,225],"application":[5],"of":[6,18,24,30,46,80,94,100,119,145,151,155,170,177,184,191,203,224],"partition-and-detect":[7,198],"framework[1],":[8],"we":[9,32,64,105,161,193],"propose":[10,65],"a":[11,44,66,87,189,209],"trajectory":[12,35,41,50,68,112,173,211],"outlier":[13,69,103,146,212],"algorithm":[14,57,70,130,196,213],"based":[15,71,214],"in":[16,123,174,197],"terms":[17],"local":[19,67,73,95,102,137,210],"density":[20,74,216],"on":[21,72,98,148,215],"basis":[23,99,150],"traditional":[26,128],"TRAOD":[27,129],"algorithm.":[28,157,186],"First":[29],"all,":[31],"preprocess":[33],"data,":[36],"which":[37,53,131],"is":[38,113,118],"to":[39,75,108],"divide":[40],"data":[42],"into":[43],"series":[45],"t-partitions":[47,83,85,204],"approximate":[49],"partitioning":[51],"algorithm,":[52],"can":[54],"improve":[55,181],"expansibility":[58],"and":[59,84,90,180,208,221],"running":[61],"efficiency.":[62],"Afterwards,":[63],"combine":[76],"two":[78],"parameters":[79],"distance":[81],"between":[82],"given":[88],"range":[89],"get":[91],"concept":[93],"density.":[96],"Then":[97],"factor,":[104],"are":[106],"able":[107],"judge":[109],"whether":[110],"an":[114],"outlier.":[115],"This":[116],"method":[117,169],"great":[120],"realistic":[121],"significance":[122],"that":[124,218],"it":[125,141],"avoids":[126],"cannot":[132],"detect":[133],"outliers":[134],"from":[135],"dense":[138],"trajectory.":[139],"Besides,":[140],"improves":[142],"quality":[144],"detection":[147],"ensuring":[152],"efficiency":[154,183,220],"meantime,":[160],"also":[162],"adopt":[163],"grid":[165,206],"index":[166],"structure":[167],"(the":[168],"obtaining":[171],"neighborhood":[176,202],"trajectory)":[179],"Finally,":[187],"through":[188],"lot":[190],"experiments":[192],"demonstrated":[194],"our":[195],"framework,":[199],"using":[200],"search":[207],"so":[217],"accuracy":[223],"results":[226],"have":[227],"been":[228],"greatly":[229],"improved.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}