dtwclust: Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance

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.

Version: 2.3.0
Depends: R (≥ 3.2.0), proxy (≥ 0.4-16), dtw, ggplot2
Imports: methods, parallel, stats, utils, caTools, flexclust, foreach, Rcpp, reshape2, rngtools
LinkingTo: Rcpp
Suggests: TSdist, TSclust, doParallel, testthat, knitr
Published: 2016-10-03
Author: Alexis Sarda-Espinosa
Maintainer: Alexis Sarda <alexis.sarda at gmail.com>
BugReports: https://github.com/asardaes/dtwclust/issues
License: GPL-3
URL: https://github.com/asardaes/dtwclust
NeedsCompilation: yes
Materials: README NEWS
In views: TimeSeries
CRAN checks: dtwclust results


Reference manual: dtwclust.pdf
Vignettes: Comparing Time-Series Clustering Algorithms in R Using the dtwclust Package
Package source: dtwclust_2.3.0.tar.gz
Windows binaries: r-devel: dtwclust_2.3.0.zip, r-release: dtwclust_2.3.0.zip, r-oldrel: dtwclust_2.3.0.zip
OS X Mavericks binaries: r-release: dtwclust_2.3.0.tgz, r-oldrel: dtwclust_2.3.0.tgz
Old sources: dtwclust archive


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