RclusTool-package: Graphical Toolbox for Clustering and Classification of Data...

Description Details Author(s) Source


Graphical toolbox for clustering and classification of data frames. It proposes a graphical interface to process clustering and classification methods on features data-frames, and to view initial data as well as resulted cluster or classes. According to the level of available labels, different approaches are proposed: unsupervised clustering, semi-supervised clustering and supervised classification. To assess the processed clusters or classes, the toolbox can import and show some supplementary data formats: either profile/time series, or images. These added information can help the expert to label clusters (clustering), or to constrain data frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by Wacquet et al. (2013) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by Wacquet et al. (2013) <doi:10.1007/978-3-642-35638-4_21>.

There is a main command: RclusToolGUI() to launch the graphical user interface of RclusTool.

Other functions can be used in R scripts and are detailed in the documentation.


Package: RclusTool
Type: Package
Version: 0.91.3
Date: 2020-01-16
License: GPL (>=2)
LazyLoad: yes


Guillaume Wacquet [aut], Pierre-Alexandre Hebert [aut, cre], Emilie Poisson [aut], Pierre Talon [aut]

Maintainer: Pierre-Alexandre Hebert hebert@univ-littoral.fr


"Constrained Spectral Embedding for K-Way Data Clustering." Pattern Recognition Letters, Wacquet, G., Caillault, E., Hamad, D., Hebert, P.-A. (2013) <doi:10.1016/j.patrec.2013.02.003>.

"Semi-supervised K-Way Spectral Clustering with Determination of Number of Clusters." Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2011, Paris, France, October 24-26, 2011, Wacquet G., Poisson-Caillault E., Hebert PA. (2013) <doi:10.1007/978-3-642-35638-4_21>.

RclusTool documentation built on Feb. 4, 2020, 5:08 p.m.