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>.
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