Data Analysis Using Rough Set and Fuzzy Rough Set Theories
The as.character method for RST rule sets
The as.list method for RST rule sets
Computation of a boundary region
The decision-relative discernibility matrix based on fuzzy rough set t...
Computation of a decision-relative discernibility matrix based on the ...
The indiscernibility relation based on fuzzy rough set theory
Computation of indiscernibility classes based on the rough set theory
The fuzzy lower and upper approximations based on fuzzy rough set theo...
Computation of lower and upper approximations of decision classes
Computation of a negative region
Positive region based on fuzzy rough set
Computation of a positive region
The fuzzy-rough nearest neighbor algorithm
The fuzzy-rough ownership nearest neighbor algorithm
The positive region based fuzzy-rough nearest neighbor algorithm
The wrapper function for discretization methods
Unsupervised discretization into intervals of equal length.
The quantile-based discretization
Supervised discretization based on the maximum discernibility heuristi...
Supervised discretization based on the local discernibility heuristic
A function for computing all decision reducts of a decision system
The DAAR heuristic for computation of decision reducts
The superreduct computation based on RST and FRST
The greedy heuristic algorithm for computing decision reducts and appr...
The greedy heuristic method for determining superreduct based on RST
The near-optimal reduction algorithm based on fuzzy rough set theory
Computing one reduct from a discernibility matrix
The permutation heuristic algorithm for computation of a decision redu...
The fuzzy QuickReduct algorithm based on FRST
QuickReduct algorithm based on RST
The reduct computation methods based on RST and FRST
The fuzzy rough instance selection algorithm
The fuzzy rough prototype selection method
Concept Closest Fit
Missing value completion by deleting instances
Global Closest Fit
Wrapper function of missing value completion
Replacing missing attribute values by the attribute mean or common val...
The most common value or mean of an attribute restricted to a concept
The predicting function for rule induction methods based on FRST
Prediction of decision classes using rule-based classifiers.
The print method of FeatureSubset objects
The print function for RST rule sets
Rule induction using the AQ algorithm
Rule induction using a version of CN2 algorithm
Generalized fuzzy rough set rule induction based on FRST
Hybrid fuzzy-rough rule and induction and feature selection
Rule induction from indiscernibility classes.
Quality indicators of RST decision rules
Rule induction using the LEM2 algorithm
Getting started with the RoughSets package
Apply for obtaining a new decision table
Converting a data.frame into a DecisionTable object
Converting custom attribute name sets into a FeatureSubset object
Reading tabular data from files.
The [. method for "RuleSetRST" objects
The summary function for an indiscernibility relation
The summary function of lower and upper approximations based on RST an...
The summary function of positive region based on RST and FRST
The summary function of rules based on FRST
The summary function of rules based on RST
The entropy measure
The gini-index measure
Rule voting by the Laplace estimate
The discernibility measure
Rule voting by counting matching rules
Rule voting by strength of the rule
Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but 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. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.