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- W4253839207 abstract "This chapter shows how to divide a set of time series into homogeneous groups of series with similar properties and how to classify a time series into one cluster among several possible clusters. It discusses some measures of distance or dissimilarities between time series. The chapter presents four procedures for clustering analysis based on variables. These methods are k-means, k-medoids that can also be used with dissimilarities, projection methods, and mixtures of distributions. When we use features of the series for the classification, then the standard cross-validation can be applied by splitting the sample into training and testing samples. When the distribution of the features is unknown, several nonparametric methods can be used for discrimination. They are nearest neighbors, support vector machines, and density estimation." @default.
- W4253839207 created "2022-05-12" @default.
- W4253839207 date "2021-03-26" @default.
- W4253839207 modified "2023-09-28" @default.
- W4253839207 title "CLUSTERING AND CLASSIFICATION OF TIME SERIES" @default.
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- W4253839207 doi "https://doi.org/10.1002/9781119417408.ch5" @default.
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