MV.conceptClosestFit {RoughSets}

Concept Closest Fit

Description

It is used for handling missing values based on the concept closest fit.

Usage

MV.conceptClosestFit(decision.table)

Arguments

decision.table

a "DecisionTable" class representing a decision table. See SF.asDecisionTable. Note: missing values are recognized as NA.

Details

This method is similar to the global closest fit method. The difference is that the original data set, containing missing attribute values, is first split into smaller data sets, each smaller data set corresponds to a concept from the original data set. More precisely, every smaller data set is constructed from one of the original concepts, by restricting cases to the concept.

Value

A class "MissingValue". See MV.missingValueCompletion.

Author(s)

Lala Septem Riza

References

J. Grzymala-Busse and W. Grzymala-Busse, "Handling Missing Attribute Values," in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds. New York : Springer, 2010, pp. 33-51

See Also

MV.missingValueCompletion

Examples

#############################################
## Example: Concept Closest Fit
#############################################
dt.ex1 <- data.frame(
     c(100.2, 102.6, NA, 99.6, 99.8, 96.4, 96.6, NA), 
     c(NA, "yes", "no", "yes", NA, "yes", "no", "yes"), 
     c("no", "yes", "no", "yes", "yes", "no", "yes", NA),
     c("yes", "yes", "no", "yes", "no", "no", "no", "yes"))
colnames(dt.ex1) <- c("Temp", "Headache", "Nausea", "Flu")
decision.table <- SF.asDecisionTable(dataset = dt.ex1, decision.attr = 4, 
                                    indx.nominal = c(2:4))
indx = MV.conceptClosestFit(decision.table)