Time series clustering along with optimized techniques related to the Dynamic Time Warping distance and its corresponding lower bounds. Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.
Version: 6.0.0 Depends: R (≥ 3.3.0), methods, proxy (≥ 0.4-16), dtw Imports: parallel, stats, utils, clue, cluster, dplyr, flexclust, foreach, ggplot2, ggrepel, rlang, Matrix (≥ 1.5-0), RSpectra, Rcpp, RcppParallel (≥ 4.4.0), reshape2, shiny, shinyjs LinkingTo: Rcpp, RcppArmadillo, RcppParallel, RcppThread Suggests: doParallel, iterators, knitr, rmarkdown, testthat Published: 2024-07-23 DOI: 10.32614/CRAN.package.dtwclust Author: Alexis Sarda-Espinosa Maintainer: Alexis Sarda <alexis.sarda at gmail.com> BugReports: https://github.com/asardaes/dtwclust/issues License: GPL-3 Copyright: see file COPYRIGHTS URL: https://github.com/asardaes/dtwclust NeedsCompilation: yes SystemRequirements: GNU make Citation: dtwclust citation info Materials: NEWS In views: TimeSeries CRAN checks: dtwclust results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=dtwclust to link to this page.
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