Software

RSES (Rough Set Exploration System) is a toolkit for analysis of table data, based on methods and algorithms coming from the area of Rough Sets. It comprises of two general components - the GUI front-end and the computational kernel. At the moment RSES is distributed freely for non-comercial use.

More about RSES - http://rseslib.mimuw.edu.pl/index.html

Rough sets in R -Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST) and also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors.

More about Rough Sets in R - https://cran.r-project.org/web/packages/RoughSets/index.html

Soft Clustering Algorithms - Based on rough clustering algorithms. It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.

More about Soft Clustering Algorithms - https://cran.r-project.org/web/packages/SoftClustering/index.html