Time series labeling algorithms based on the K-nearest neighbors' frequencies

dc.contributor.author	Nasibov, E.N.
dc.contributor.author	Peker, S.
dc.date.accessioned	2021-01-25T20:51:20Z
dc.date.available	2021-01-25T20:51:20Z
dc.date.issued	2011
dc.identifier	10.1016/j.eswa.2010.09.147
dc.identifier.issn	09574174
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-79151483731&doi=10.1016%2fj.eswa.2010.09.147&partnerID=40&md5=2473f55e4aad61cedbf26234264bf724
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/10627
dc.description.abstract	In the current paper, time series labeling task is analyzed and some solution algorithms are presented. In these algorithms, fuzzy c-means clustering, which is one of the unsupervised learning methods, is used to obtain the labels of the time series. Then
dc.language.iso	English
dc.publisher	Expert Systems with Applications
dc.title	Time series labeling algorithms based on the K-nearest neighbors' frequencies
dc.type	Article
dc.relation.firstpage	5028
dc.relation.lastpage	5035
dc.relation.volume	38
dc.relation.issue	5
dc.description.affiliations	Department of Computer Science, Faculty of Sciences, Dokuz Eylul University, 35160 Buca, Izmir, Turkey; Department of Statistics, Faculty of Science and Letters, Yasar University, 35100 Bornova, Izmir, Turkey

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