dtwclustTimings

This is the list with data frames containing the results of the timing experiments vignette
included with dtwclust. See <code>browseVignettes("dtwclust")</code>.

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.

Alexis Sarda

dtwclust

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

dtwclustTimings function

The results are organized into different data frames and saved in one list with nested lists.
For more details, refer to the included vignette or the scripts available at
<a href="https://github.com/asardaes/dtwclust/tree/master/timing-experiments">https://github.com/asardaes/dtwclust/tree/master/timing-experiments</a>.

Format

Results of timing experiments — dtwclustTimings

The results are organized into different data frames and saved in one list with nested lists.
For more details, refer to the included vignette or the scripts available at
<a href='https://github.com/asardaes/dtwclust/tree/master/timing-experiments'>https://github.com/asardaes/dtwclust/tree/master/timing-experiments</a>.

dtwclustTimings: Results of timing experiments

Description

Arguments

Format