{"id":"https://openalex.org/W4297858020","doi":"https://doi.org/10.29007/ffss","title":"A Possibilistic Fuzzy Approach for Novelty Detection with Automatically Adjusted Number of Clusters","display_name":"A Possibilistic Fuzzy Approach for Novelty Detection with Automatically Adjusted Number of Clusters","publication_year":2022,"publication_date":"2022-09-20","ids":{"openalex":"https://openalex.org/W4297858020","doi":"https://doi.org/10.29007/ffss"},"language":"en","primary_location":{"id":"doi:10.29007/ffss","is_oa":true,"landing_page_url":"https://doi.org/10.29007/ffss","pdf_url":"https://easychair.org/publications/open/mSms","source":{"id":"https://openalex.org/S4220651395","display_name":"EPiC series in computing","issn_l":"2398-7340","issn":["2398-7340"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPiC Series in Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://easychair.org/publications/open/mSms","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035659737","display_name":"Shakhnaz Akhmedova","orcid":"https://orcid.org/0000-0003-2927-1974"},"institutions":[{"id":"https://openalex.org/I56490300","display_name":"Siberian State Aerospace University","ror":"https://ror.org/05r50mj29","country_code":"RU","type":"education","lineage":["https://openalex.org/I56490300"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Shakhnaz Akhmedova","raw_affiliation_strings":["Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation"],"affiliations":[{"raw_affiliation_string":"Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation","institution_ids":["https://openalex.org/I56490300"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002679497","display_name":"Vladimir Stanovov","orcid":"https://orcid.org/0000-0002-1695-5798"},"institutions":[{"id":"https://openalex.org/I56490300","display_name":"Siberian State Aerospace University","ror":"https://ror.org/05r50mj29","country_code":"RU","type":"education","lineage":["https://openalex.org/I56490300"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Vladimir Stanovov","raw_affiliation_strings":["Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation"],"affiliations":[{"raw_affiliation_string":"Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation","institution_ids":["https://openalex.org/I56490300"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035349875","display_name":"Eugene Semenkin","orcid":"https://orcid.org/0000-0002-3776-5707"},"institutions":[{"id":"https://openalex.org/I56490300","display_name":"Siberian State Aerospace University","ror":"https://ror.org/05r50mj29","country_code":"RU","type":"education","lineage":["https://openalex.org/I56490300"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Eugene Semenkin","raw_affiliation_strings":["Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation"],"affiliations":[{"raw_affiliation_string":"Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation","institution_ids":["https://openalex.org/I56490300"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058300696","display_name":"Sophia Vishnevskaya","orcid":null},"institutions":[{"id":"https://openalex.org/I56490300","display_name":"Siberian State Aerospace University","ror":"https://ror.org/05r50mj29","country_code":"RU","type":"education","lineage":["https://openalex.org/I56490300"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Sophia Vishnevskaya","raw_affiliation_strings":["Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation"],"affiliations":[{"raw_affiliation_string":"Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russian Federation","institution_ids":["https://openalex.org/I56490300"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5035659737"],"corresponding_institution_ids":["https://openalex.org/I56490300"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09943616,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"81","issue":null,"first_page":"65","last_page":"52"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.995199978351593,"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/benchmark","display_name":"Benchmark (surveying)","score":0.681316077709198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6447283029556274},{"id":"https://openalex.org/keywords/novelty-detection","display_name":"Novelty detection","score":0.6422396302223206},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.6163020730018616},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6061014533042908},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.5542086958885193},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.520763635635376},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.503358781337738},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49840545654296875},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4785645306110382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47286152839660645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40447667241096497},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35449156165122986},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3287390172481537}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.681316077709198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6447283029556274},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.6422396302223206},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.6163020730018616},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6061014533042908},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.5542086958885193},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.520763635635376},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.503358781337738},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49840545654296875},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4785645306110382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47286152839660645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40447667241096497},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35449156165122986},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3287390172481537},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.29007/ffss","is_oa":true,"landing_page_url":"https://doi.org/10.29007/ffss","pdf_url":"https://easychair.org/publications/open/mSms","source":{"id":"https://openalex.org/S4220651395","display_name":"EPiC series in computing","issn_l":"2398-7340","issn":["2398-7340"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPiC Series in Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.29007/ffss","is_oa":true,"landing_page_url":"https://doi.org/10.29007/ffss","pdf_url":"https://easychair.org/publications/open/mSms","source":{"id":"https://openalex.org/S4220651395","display_name":"EPiC series in computing","issn_l":"2398-7340","issn":["2398-7340"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPiC Series in Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321079","display_name":"Russian Foundation for Basic Research","ror":"https://ror.org/02mh1ke95"},{"id":"https://openalex.org/F4320321181","display_name":"Austrian Science Fund","ror":"https://ror.org/013tf3c58"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4297858020.pdf","grobid_xml":"https://content.openalex.org/works/W4297858020.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1576317787","https://openalex.org/W1617839798","https://openalex.org/W1669052316","https://openalex.org/W2046162917","https://openalex.org/W2113076747","https://openalex.org/W2115627867","https://openalex.org/W2120688485","https://openalex.org/W2125836384","https://openalex.org/W2167428023","https://openalex.org/W2219140802","https://openalex.org/W2801670977","https://openalex.org/W2897883625","https://openalex.org/W2929291387","https://openalex.org/W2971503667","https://openalex.org/W3025682415","https://openalex.org/W3082200085","https://openalex.org/W3208688400","https://openalex.org/W4300942247","https://openalex.org/W6677213129","https://openalex.org/W6677772535","https://openalex.org/W6684727711","https://openalex.org/W6688847702","https://openalex.org/W7064111672"],"related_works":["https://openalex.org/W2064636555","https://openalex.org/W2585503716","https://openalex.org/W1939982668","https://openalex.org/W2105014086","https://openalex.org/W2076090200","https://openalex.org/W3025682415","https://openalex.org/W2081173909","https://openalex.org/W4389009659","https://openalex.org/W4312933423","https://openalex.org/W2604316291"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3],"new":[4,70],"modification":[5,18,115,171],"of":[6,24,28,31,74,112,142,198],"the":[7,22,25,29,40,56,75,84,108,113,122,126,135,151,153,158,164,169,178,187,191,199],"Possibilistic":[8],"Fuzzy":[9],"multiclass":[10],"Novelty":[11],"Detector":[12],"for":[13,33,90,203],"data":[14],"streams":[15],"(PFuzzND).":[16],"Mentioned":[17],"is":[19,175],"based":[20],"on":[21],"implementation":[23],"automated":[26],"adjustment":[27],"number":[30,141],"clusters":[32,143],"each":[34],"class":[35,63],"(determined":[36],"beforehand":[37],"or":[38,72],"during":[39],"novelty":[41,91],"detection":[42,92],"procedure)":[43],"to":[44,48,68,95,106,150,163,177],"improve":[45],"algorithm\u2019s":[46],"ability":[47],"divide":[49],"objects":[50],"into":[51],"small":[52],"groups.":[53],"As":[54],"result,":[55],"proposed":[57,170],"approach":[58,193],"generates":[59],"models":[60],"with":[61,100,121,138],"flexible":[62],"boundaries,":[64],"which":[65,181],"are":[66,78],"capable":[67],"identify":[69],"classes":[71],"extensions":[73],"ones":[76],"that":[77,134,168],"already":[79],"known":[80],"as":[81,83],"well":[82],"outliers.":[85],"Proposed":[86],"possibilistic":[87],"fuzzy":[88],"algorithm":[89,166,202],"was":[93,132],"used":[94,196],"solve":[96],"various":[97],"benchmark":[98],"problems":[99],"synthetically":[101],"generated":[102],"datasets.":[103],"In":[104],"order":[105],"show":[107],"workability":[109],"and":[110,157],"efficiency":[111],"introduced":[114],"its":[116],"results":[117,123,147],"were":[118],"also":[119,184],"compared":[120],"obtained":[124],"by":[125],"original":[127,165,200],"PFuzzND":[128,136,188,201],"algorithm.":[129],"Thus,":[130],"it":[131,173],"established":[133],"technique":[137],"automatically":[139],"adjusted":[140],"allows":[144],"achieving":[145],"better":[146],"in":[148],"regards":[149],"accuracy,":[152],"Macro":[154],"F-score":[155],"metric":[156],"unknown":[159],"rate":[160],"measure.":[161],"Comparison":[162],"showed":[167],"outperforms":[172],"but":[174],"sensitive":[176],"parameter":[179],"settings,":[180],"can":[182,194],"be":[183,195],"said":[185],"about":[186],"method.":[189],"Therefore,":[190],"MPFuzzND":[192],"instead":[197],"other":[204],"classification":[205],"problems.":[206]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}