{"id":"https://openalex.org/W3174270358","doi":"https://doi.org/10.1109/icccsp52374.2021.9465539","title":"Notice of Removal: Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm","display_name":"Notice of Removal: Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm","publication_year":2021,"publication_date":"2021-05-24","ids":{"openalex":"https://openalex.org/W3174270358","doi":"https://doi.org/10.1109/icccsp52374.2021.9465539","mag":"3174270358"},"language":"en","primary_location":{"id":"doi:10.1109/icccsp52374.2021.9465539","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccsp52374.2021.9465539","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP)","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/A5086543035","display_name":"Vardhan Shorewala","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Vardhan Shorewala","raw_affiliation_strings":["Dhirubhai Ambani International School,Mumbai,India","Dhirubhai Ambani International School, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"Dhirubhai Ambani International School,Mumbai,India","institution_ids":[]},{"raw_affiliation_string":"Dhirubhai Ambani International School, Mumbai, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5086543035"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1197,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.82155021,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"115","last_page":"121"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9914000034332275,"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"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.882016122341156},{"id":"https://openalex.org/keywords/silhouette","display_name":"Silhouette","score":0.6474249362945557},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6339629888534546},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6116785407066345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5415765047073364},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5054271221160889},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4780062735080719},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46050095558166504},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44824063777923584},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4361991286277771},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.41539251804351807},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.41493844985961914},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14216965436935425}],"concepts":[{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.882016122341156},{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.6474249362945557},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6339629888534546},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6116785407066345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5415765047073364},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5054271221160889},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4780062735080719},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46050095558166504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44824063777923584},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4361991286277771},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.41539251804351807},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.41493844985961914},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14216965436935425},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccsp52374.2021.9465539","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccsp52374.2021.9465539","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1552339598","https://openalex.org/W1673310716","https://openalex.org/W1970655212","https://openalex.org/W1987971958","https://openalex.org/W2005314985","https://openalex.org/W2035006576","https://openalex.org/W2049058890","https://openalex.org/W2051224630","https://openalex.org/W2061240327","https://openalex.org/W2083121736","https://openalex.org/W2085487226","https://openalex.org/W2103459159","https://openalex.org/W2129249398","https://openalex.org/W2137130182","https://openalex.org/W2166999932","https://openalex.org/W2331052961","https://openalex.org/W2395916081","https://openalex.org/W2498631646","https://openalex.org/W2592377938","https://openalex.org/W2800637158","https://openalex.org/W2886371990","https://openalex.org/W2972155310","https://openalex.org/W2975876552","https://openalex.org/W3041834803","https://openalex.org/W3093362704","https://openalex.org/W4242401062","https://openalex.org/W4254607142","https://openalex.org/W6632829031","https://openalex.org/W6637131181","https://openalex.org/W6675675289","https://openalex.org/W6780475912","https://openalex.org/W6986189286","https://openalex.org/W7047381192"],"related_works":["https://openalex.org/W1994775821","https://openalex.org/W2012019886","https://openalex.org/W2091133150","https://openalex.org/W2945869148","https://openalex.org/W2611195251","https://openalex.org/W2398781203","https://openalex.org/W2009279505","https://openalex.org/W4206503171","https://openalex.org/W4366711670","https://openalex.org/W2972216353"],"abstract_inverted_index":{"This":[0],"paper":[1],"explores":[2],"a":[3,17,21,76,85],"unified":[4],"approach":[5,23],"to":[6,134],"the":[7,12,25,37,51,58,66,73,89,94,107,111,115,145,148,166,176,179],"improvement":[8],"of":[9,14,27,69,91,147,178],"clusters":[10,31],"and":[11,29,57,114,124,130,150,157,168,173],"detection":[13,90],"anomalies":[15,92],"in":[16,93,138,164,175],"dataset.":[18,95,182],"It":[19,40,160],"presents":[20],"novel":[22,86],"for":[24,46,88],"formation":[26],"tighter":[28],"better":[30],"than":[32],"traditional":[33],"methods":[34],"such":[35,49,105],"as":[36,50,84,106],"K-means":[38,83],"algorithm.":[39],"is":[41,100,120],"evaluated":[42,101],"against":[43],"intrinsic":[44],"measures":[45,104],"unsupervised":[47],"learning":[48],"silhouette":[52],"coefficient,":[53],"Calinski":[54],"Harabasz":[55],"index,":[56],"Davies":[59],"Bouldin":[60],"index.":[61],"The":[62,79,96,118,141],"proposed":[63,97,142],"method":[64,80,87],"decreases":[65],"intracluster":[67],"variance":[68,74,146],"N":[70],"clusters,":[71],"until":[72],"approaches":[75],"global":[77],"minimum.":[78],"also":[81,162],"extends":[82],"algorithm's":[98],"performance":[99],"by":[102,155,171],"extrinsic":[103],"Jaccard":[108],"similarity":[109],"score,":[110],"V-measure":[112],"cluster,":[113],"F1":[116,169],"Score.":[117],"algorithm":[119,143],"tested":[121],"upon":[122],"synthetic":[123,149],"real":[125,151],"datasets,":[126],"UCI":[127,131],"Breast":[128],"Cancer":[129],"Wine":[132,153,180],"Quality,":[133,154],"showcase":[135],"its":[136],"effectiveness":[137],"both":[139],"cases.":[140],"reduced":[144],"dataset,":[152],"18.7%":[156],"88.1%":[158],"respectively.":[159],"was":[161],"effective":[163],"increasing":[165],"accuracy":[167],"score":[170],"22.5%":[172],"20.8%":[174],"case":[177],"Quality":[181]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2021-07-05T00:00:00"}
