Efendi N. NasibovSinem PekerNasibov, Efendi N.Peker, Sinem2025-10-062011095741740957-41741873-679310.1016/j.eswa.2010.09.1472-s2.0-79151483731https://www.scopus.com/inward/record.uri?eid=2-s2.0-79151483731&doi=10.1016%2Fj.eswa.2010.09.147&partnerID=40&md5=f3cd22584730590df3bc15dbb60911b4https://gcris.yasar.edu.tr/handle/123456789/10237https://doi.org/10.1016/j.eswa.2010.09.147In 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 K-nearest neighborhood (KNN) rule is performed on the labels to obtain more relevant smooth intervals. As an application the handled labeling algorithms are performed on bispectral index (BIS) data which are time series measures of brain activity. Finally smoothing process is found useful in the estimation of sedation stage labels. © 2010 Elsevier Ltd. All rights reserved. © 2011 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessBispectral Index, Clustering, Fcm, K-nearest Neighbor, Time Series, Bispectral Index, Brain Activity, Clustering, Fcm, Fuzzy C Means Clustering, K-nearest Neighborhoods, K-nearest Neighbors, Labeling Algorithms, Smoothing Process, Solution Algorithms, Unsupervised Learning Method, Brain, Cluster Analysis, Membership Functions, Text Processing, Time Series, Unsupervised Learning, Clustering AlgorithmsBispectral index, Brain activity, Clustering, FCM, Fuzzy C means clustering, K-nearest neighborhoods, K-nearest neighbors, Labeling algorithms, Smoothing process, Solution algorithms, Unsupervised learning method, Brain, Cluster analysis, Membership functions, Text processing, Time series, Unsupervised learning, Clustering algorithmsFCMTime SeriesClusteringK-Nearest NeighborBispectral IndexArticle