ScatterR-gui is a graphical user interface for the basic use of Scatter-R. Scatter-R is an implementation of Scatter-algorithm in R.
The ScatterR-gui provides a graphical user interface for Scatter-R. Detailed description about the Scatter-R project can be found in the project wiki.
Scatter is an algorithm useful to determine if the dataset or parts of it has such information that can be successfully used for classification and class prediction. [1] It can also used in dimensionality reduction as shown by Saarikoski et al. in [2].
It works simply by traversing the dataset from a randomly chosen starting case to always closest neighbour recording the class label. In this way a collection vector is produced and the label change count is calculated from the collection vector. Then, scatter value is calculated as a proportion of the label changes v
and theoretical maximum number of label changes w
, thus the equation for Scatter value S = v / w
. [1]
Scatter value is also used to calculate separation power, which is the difference between random situation, i.e. the labels of the current dataset is randomly distributed, and the current situation. The equation for separation power is F = z - s
, where z
is the scatter value for random situation.[1]
ScatterR-gui()
function summons a graphical user interface
to assist in using the scatter algorithm
and the associated preprocessing function scatter.preprocess
.
ScatterR-gui()
provides an easy-to-use graphical interface for
reading data files,
making selections related to preprocessing
and the scatter algorithm calculation,
calling the scatter.preprocess
and scatter
functions
with the selected arguments
and ultimately saving the results.
See installation instructions here.
ScatterR-gui()
opens the ScatterR-gui main window.
In the following, the GUI elements and their functionality is described, starting from the top-most elements.
scatter.gui
shows a dialog where it is possible to save the result.dget()
function.[1] Juhola, M., & Siermala, M. (2012). A scatter method for data and variable importance evaluation. Integrated Computer-Aided Engineering, 19 (2), 137–149. http://doi.org/10.3233/ICA-2011-0385
[2] Saarikoski, J., Laurikkala, J., Järvelin, K., Siermala, M., & Juhola, M. (2014). Dimensionality reduction in text classification using scatter method. International Journal of Data Mining, Modelling and Management, 6 (1), 1. http://doi.org/10.1504/IJDMMM.2014.059978
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.