Display a shiny user interface to do clustering based on the provided series.
Time series in the formats accepted by
More arguments for
This part of the app is simply to see some basic characteristics of the provided series and
plot some of them. The field for integer IDs expects a valid R expression that specifies which
series should be plotted. Multivariate series are plotted with each variable in a
This part of the app wraps
tsclust(), so you should be familiar with it. Some remarks:
Specifying a custom centroid or hierarchical method expects the name of a function available
in the R session (without quotes). Naturally, any required package should be loaded before
interactive_clustering. For example, if you want to use
should load cluster beforehand.
A random seed of 0 means that it will be left as
NULL when calling
The input fields for Extra parameters (distance, centroid and ellipsis) expect a comma-
separated sequence of key-value pairs. For example:
window.size = 10L,
trace = TRUE. You
should be able to pass any variables available in the R session's global environment.
Regarding plot parameters:
Clusters field is like the integer IDs from the Explore section.
Labels field is passed to the plot method (see TSClusters-methods). You can specify
several values like with the Extra parameters, e.g.:
nudge_x = 10,
nudge_y = 1. You can
type an empty space to activate them with the defaults, and delete everything to hide them.
Note that the location of the labels is random each time.
The plot area reacts to the plot parameters, but the actual clustering with
be executed until you click the
Cluster! button. The plot can take a couple of seconds to
load! Plotting multivariate series might generate warnings about missing values, they can be
Some of the control parameters are disabled when dtwclust detects them automatically.
The cross-distance matrix is cached so that it can be re-used when appropriate. The cached version is invalidated automatically when necessary.
This part of the app provides results of the current clustering. External CVIs can be
calculated if the name of a variable with the ground truth is provided (see
Tracing is printed to the console.
## Not run: interactive_clustering(CharTrajMV) ## End(Not run)
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